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Personalization of Web Search Results Using Term, Category, and Link-Based User Profiles

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US20100228715A1
US20100228715A1 US12778869 US77886910A US20100228715A1 US 20100228715 A1 US20100228715 A1 US 20100228715A1 US 12778869 US12778869 US 12778869 US 77886910 A US77886910 A US 77886910A US 20100228715 A1 US20100228715 A1 US 20100228715A1
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user
search
profile
documents
document
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Stephen R. Lawrence
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Lawrence Stephen R
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    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/30Information retrieval; Database structures therefor ; File system structures therefor
    • G06F17/30861Retrieval from the Internet, e.g. browsers
    • G06F17/30864Retrieval from the Internet, e.g. browsers by querying, e.g. search engines or meta-search engines, crawling techniques, push systems
    • G06F17/30867Retrieval from the Internet, e.g. browsers by querying, e.g. search engines or meta-search engines, crawling techniques, push systems with filtering and personalisation
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/30Information retrieval; Database structures therefor ; File system structures therefor
    • G06F17/30011Document retrieval systems
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/30Information retrieval; Database structures therefor ; File system structures therefor
    • G06F17/30286Information retrieval; Database structures therefor ; File system structures therefor in structured data stores
    • G06F17/30386Retrieval requests
    • G06F17/30424Query processing
    • G06F17/30522Query processing with adaptation to user needs
    • G06F17/3053Query processing with adaptation to user needs using ranking
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/30Information retrieval; Database structures therefor ; File system structures therefor
    • G06F17/30286Information retrieval; Database structures therefor ; File system structures therefor in structured data stores
    • G06F17/30386Retrieval requests
    • G06F17/30554Query result display and visualisation

Abstract

A system and method for creating a user profile and for using the user profile to order search results returned by a search engine. The user profile is based on search queries submitted by a user, the user's specific interaction with the documents identified by the search engine and personal information provided by the user. Terms for the user profile may be selected from the documents accessed by the user by performing paragraph sampling or context analysis. Generic scores associated with the search results are modulated by the user profile to measure their relevance to a user's preference and interest. The search results are re-ordered accordingly so that the most relevant results appear on the top of the list. User profiles can be created and/or stored on the client side or server side of a client-server network environment.

Description

    RELATED APPLICATIONS
  • [0001]
    This application is a continuation of U.S. patent application Ser. No. 10/676,711, filed Sep. 30, 2003, entitled “Personalization of Web Search,” which is incorporated herein by reference in its entirety.
  • FIELD OF THE INVENTION
  • [0002]
    The present invention relates generally to the field of a search engine in a computer network system, in particular to system and method of creating a user profile for a user of a search engine and using the user profile to customize search results in response to search queries submitted by the user.
  • BACKGROUND OF THE INVENTION
  • [0003]
    Search engines provide a powerful source of indexed documents from the Internet (or an intranet) that can be rapidly scanned in response to a search query submitted by a user. Such a query is usually very short (on average about two to three words). As the number of documents accessible via the Internet grows, the number of documents that match the query may also increase. However, not every document matching the query is equally important from the user's perspective. As a result, a user is easily overwhelmed by an enormous number of documents returned by a search engine, if the engine does not order the search results based on their relevance to the user's query.
  • [0004]
    One approach to improving the relevance of search results to a search query is to use the link structure of different web pages to compute global “importance” scores that can be used to influence the ranking of search results. This is sometimes referred to as the PageRank algorithm. A more detailed description of the PageRank algorithm can be found in the article “The Anatomy of a Large-Scale Hypertextual Search Engine” by S. Brin and L. Page, 7th International World Wide Web Conference, Brisbane, Australia and U.S. Pat. No. 6,285,999, both of which are hereby incorporated by reference as background information.
  • [0005]
    An important assumption in the PageRank algorithm is that there is a “random surfer” who starts his web surfing journey at a randomly picked web page and keeps clicking on the links embedded in the web pages, never hitting the “back” button. Eventually, when this random surfer gets bored of the journey, he may re-start a new journey by randomly picking another web page. The probability that the random surfer visits (i.e., views or downloads) a web page depends on the web page's page rank.
  • [0006]
    From an end user's perspective, a search engine using the PageRank algorithm treats a search query the same way no matter who submits the query, because the search engine does not ask the user to provide any information that can uniquely identify the user. The only factor that affects the search results is the search query itself, e.g., how many terms are in the query and in what order. The search results are a best fit for the interest of an abstract user, the “random surfer”, and they are not be adjusted to fit a specific user's preferences or interests.
  • [0007]
    In reality, a user like the random surfer never exists. Every user has his own preferences when he submits a query to a search engine. The quality of the search results returned by the engine has to be evaluated by its users' satisfaction. When a user's preferences can be well defined by the query itself, or when the user's preference is similar to the random surfer's preference with respect to a specific query, the user is more likely to be satisfied with the search results. However, if the user's preference is significantly biased by some personal factors that are not clearly reflected in a search query itself, or if the user's preference is quite different from the random user's preference, the search results from the same search engine may be less useful to the user, if not useless.
  • [0008]
    As suggested above, the journey of the random surfer tends to be random and neutral, without any obvious inclination towards a particular direction. When a search engine returns only a handful of search results that match a query, the order of the returned results is less significant because the requesting user may be able to afford the time to browse each of them to discover the items most relevant to himself. However, with billions of web pages connected to the Internet, a search engine often returns hundreds or even thousands of documents that match a search query. In this case, the ordering of the search results is very important. A user who has a preference different from that of the random surfer may not find what he is looking for in the first five to ten documents listed in the search results. When that happens, the user is usually left with two options: (1) either spending the time required to review more of the listed documents so as to locate the relevant documents; or (2) refining the search query so as to reduce the number of documents that match the query. Query refinement is often a non-trivial task, sometimes requiring more knowledge of the subject or more expertise with search engines than the user possesses, and sometimes requiring more time and effort than the user is willing to expend.
  • [0009]
    For example, assume that a user submits to a search engine a search query having only one term “blackberry”. Without any other context, on the top of a list of documents returned by a PageRank-based search engine may be a link to www.blackberry.net, because this web page has the highest page rank. However, if the query requester is a person with interests in foods and cooking, it would be more useful to order the search results so as to include at the top of the returned results web pages with recipes or other food related text, pictures or the like. It would be desirable to have a search engine that is able to reorder its search results, or to otherwise customize the search results, so as to emphasize web pages that are most likely to be of interest to the person submitting the search query. Further, it would be desirable for such a system to require minimal input from individual users, operating largely or completely without explicit input from the user with regard to the user's preferences and interests. Finally, it would be desirable for such a system to meet users' requirements with respect to security and privacy.
  • SUMMARY
  • [0010]
    A search engine utilizes user profiles to customize search results. A user profile comprises multiple items that characterize a user's search preference. These items are extracted from various information sources, including previous search queries submitted by the user, links from or to the documents identified by the previous queries, sampled content from the identified documents as well as personal information implicitly or explicitly provided by the user.
  • [0011]
    When the search engine receives a search query from a user, it first identifies a set of documents that match the search query. Each document is associated with a generic rank based on the document's page rank, the text associated with the document, and the search query. The search engine also identifies the user's profile and correlates the user profile with each of the identified documents. The correlation between a document and the user profile produces a profile rank for the document, indicating the relevance of the document to the user. The search engine then combines the document's generic rank and profile rank into a personalized rank. Finally, the documents are ordered according to their personalized ranks.
  • [0012]
    In one embodiment, a user profile may comprise a plurality of sub-profiles, each sub-profile characterizing the user's interest from a different perspective. A term-based profile comprises a plurality of terms, each term carrying a weight indicative of its importance relative to other terms. A category-based profile comprises multiple categories, optionally organized into a hierarchical map. The user's search preferences may be associated with at least a subset of the multiple categories, each category having an associated weight indicating the user's interest in the documents falling into this category. There may be multiple category-based profiles for a user. In some embodiments, the sub-profiles include a link-based profile, which includes a plurality of links that are, directly or indirectly, related to identified documents, each link having a weight indicating the importance of the link. Links in the link-based profile may be further organized with respect to different hosts and domains.
  • [0013]
    The present invention, including user profile construction and search results re-ordering and/or scoring, can be implemented on either the client side or the server side of a client-server network environment.
  • BRIEF DESCRIPTION OF THE DRAWINGS
  • [0014]
    The aforementioned features and advantages of the invention as well as additional features and advantages thereof will be more clearly understood hereinafter as a result of a detailed description of preferred embodiments of the invention when taken in conjunction with the drawings.
  • [0015]
    FIG. 1 illustrates a client-server network environment.
  • [0016]
    FIG. 2 illustrates multiple sources of user information and their relationship to a user profile.
  • [0017]
    FIG. 3 is an exemplary data structure that may be used for storing term-based profiles for a plurality of users.
  • [0018]
    FIG. 4A is an exemplary category map that may be used for classifying a user's past search experience.
  • [0019]
    FIG. 4B is an exemplary data structure that may be used for storing category-based profiles for a plurality of users.
  • [0020]
    FIG. 5 is an exemplary data structure that may be used for storing link-based profiles for a plurality of users.
  • [0021]
    FIG. 6 is a flowchart illustrating paragraph sampling.
  • [0022]
    FIG. 7A is a flowchart illustrating context analysis.
  • [0023]
    FIG. 7B depicts a process of identifying important terms using context analysis.
  • [0024]
    FIG. 8 illustrates a plurality of exemplary data structures that may be used for storing information about documents after term-based, category-based and/or link-based analyses, respectively.
  • [0025]
    FIG. 9A is a flowchart illustrating a personalized web search process according to one embodiment.
  • [0026]
    FIG. 9B is a flowchart illustrating a personalized web search process according to another embodiment.
  • [0027]
    FIG. 10 is a block diagram of a personalized search engine.
  • [0028]
    Like reference numerals refer to corresponding parts throughout the several views of the drawings.
  • DESCRIPTION OF EMBODIMENTS
  • [0029]
    The embodiments discussed below include systems and methods that create a user profile based a user's past experience with a search engine and then use the user profile to rank search results in response to search queries provided by the user.
  • [0030]
    FIG. 1 provides an overview of a typical client-server network environment 100 in which the present invention may be implemented. A plurality of clients 102 are connected to a search engine system 107 through a network 105, e.g., the Internet. Search engine system 107 comprises one or more search engines 104. A search engine 104 is responsible for processing a search query submitted by a client 102, generating search results in accordance with the search query and returning the results to the client. Search engine system 107 may also comprise one or more content servers 106 and one or more user profile servers 108. A content server 106 stores a large number of indexed documents retrieved from different websites. Alternately, or in addition, the content server 106 stores an index of documents stored on various websites. In one embodiment, each indexed document is assigned a page rank according to the document's link structure. The page rank serves as a query independent measure of the document's importance. A search engine 104 communicates with one or more content servers 106 to select a plurality of documents in response to a specific search query. The search engine assigns a score to each document based on the document's page rank, the text associated with the document, and the search query.
  • [0031]
    A user profile server 108 stores a plurality of user profiles. Each profile includes information that uniquely identifies a user as well as his previous search experience and personal information, which can be used to refine search results in response to the search queries submitted by this user. Different approaches are available for user profile construction. For example, a user profile can be created by requiring a first-time user to fill in a form or answer a survey. This approach may be useful in certain applications such as opening a bank account. But it is hardly a favorable one in the context of a search engine. First, a user's interaction with a search engine is usually a dynamic process. As time goes on, the user's interests may change. This change may be reflected by the search queries submitted by the user, or by the user's handling of the search results, or both. The user's answers to questions on a form tend to become less useful over time, unless the user chooses to update his answers periodically. Unlike an occasional update of phone number in the case of an on-line bank account, frequent updates of a user profile in the case of a search engine significantly affect its user friendliness, which is an important consideration when a user chooses among the search engines currently available. Further, it is known that users are reluctant to provide explicit feedback, such as filling out of a form, as many users find it too burdensome. Thus, while some users may provide explicit feedback on their interests, it is desirable to have a procedure for implicitly obtaining information about the user's interests without requiring any explicit or new actions by the user.
  • [0032]
    It is has been observed that a search engine user's past search activities provide useful hints about the user's personal search preferences. FIG. 2 provides a list of sources of user information that are beneficial for user profile construction. For example, previously submitted search queries 201 are very helpful in profiling a user's interests. If a user has submitted multiple search queries related to diabetes, it is more likely than not that this is a topic of interest to the user. If the user subsequently submits a query including the term “organic food”, it can be reasonably inferred that he may be more interested in those organic foods that are helpful in fighting diabetes. Similarly, the universal resource locators (URL) 203 associated with the search results in response to the previous search queries and their corresponding anchor texts 205, especially for search result items that have been selected or “visited” by the user (e.g., downloaded or otherwise viewed by the user), are helpful in determining the user's preferences. When a first page contains a link to a second page, and the link has text associated with it (e.g., text neighboring the link), the text associated with the link is called “anchor text” with respect to the second page. Anchor text establishes a relationship between the text associated with a URL link in a document and another document to which the URL link points. The advantages of anchor text include that it often provides an accurate description of the document to which the URL link points, and it can be used to index documents that cannot be indexed by a text-based search engine, such as images or databases.
  • [0033]
    After receiving search results, the user may click on some of the URL links, thereby downloading the documents referenced by those links, so as to learn more details about those documents. Certain types of general information 207 can be associated with a set of user selected or use identified documents. For purposes of forming a user profile, the identified documents from which information is derived for inclusion in the user profile may include: documents identified by search results from the search engine, documents accessed (e.g., viewed or downloaded, for example using a browser application) by the user (including documents not identified in prior search results), documents linked to the documents identified by search results from the search engine, and documents linked to the documents accessed by the user, or any subset of such documents.
  • [0034]
    The general information 207 about the identified documents may answer questions such as, what is the format of the document? Is it in hypertext markup language (HTML), plain text, portable document format (PDF), or Microsoft Word? What is the topic of the document? Is it about science, health or business? This information is also helpful in profiling the user's interests. In addition, information about a user's activities 209 with respect to the user selected documents (sometimes herein call the identified documents), such as how long the user spent viewing the document, the amount of scrolling activity on the document, and whether the user has printed, saved or bookmarked the document, also suggests the importance of the document to the user as well as the user's preferences. In some embodiments, information about user activities 209 is used both when weighting the importance of information extracted or derived from the user identified documents. In some embodiments, information about user activities 209 is used to determine which of the user identified documents to use as the basis for deriving the user profile. For example, information 209 may be used to select only documents that received significant user activity (in accordance with predefined criteria) for generating the user profile, or information 209 may be used to exclude from the profiling process documents that the user viewed for less than a predefined threshold amount of time.
  • [0035]
    Finally, the content of the identified documents from previous search activities is a rich source of information about a user's interests and preferences. Key terms appearing in the identified documents and their frequencies with which they appear in the identified documents are not only useful for indexing the document, but are also a strong indication of the user's personal interests, especially when they are combined with other types of user information discussed above. In one embodiment, instead of the whole documents, sampled content 211 from the identified documents is extracted for the purpose of user profile construction, to save storage space and computational cost. In another embodiment, various information related to the identified documents may be classified to constitute category information 213 about the identified documents. More discussion about content sampling, the process of identifying key terms in an identified document and the usage of the category information is provided below.
  • [0036]
    Optionally, a user may choose to offer personal information 215, including demographic and geographic information associated with the user, such as the user's age or age range, educational level or range, income level or range, language preferences, marital status, geographic location (e.g., the city, state and country in which the user resides, and possibly also including additional information such as street address, zip code, and telephone area code), cultural background or preferences, or any subset of these. Compared with other types of personal information such as a user's favorite sports or movies that are often time varying, this personal information is more static and more difficult to infer from the user's search queries and search results, but may be crucial in correctly interpreting certain queries submitted by the user. For example, if a user submits a query containing “Japanese restaurant”, it is very likely that he may be searching for a local Japanese restaurant for dinner. Without knowing the user's geographical location, it is hard to order the search results so as to bring to the top those items that are most relevant to the user's true intention. In certain cases, however, it is possible to infer this information. For example, users often select results associated with a specific region corresponding to where they live.
  • [0037]
    Creating a user profile 230 from the various sources of user information is a dynamic and complex process. In some embodiments, the process is divided into sub-processes. Each sub-process produces one type of user profile characterizing a user's interests or preferences from a particular perspective. They are:
      • a term-based profile 231—this profile represents a user's search preferences with a plurality of terms, where each term is given a weight indicating the importance of the term to the user;
      • a category-based profile 233—this profile correlates a user's search preferences with a set of categories, which may be organized in a hierarchal fashion, with each category being given a weight indicating the extent of correlation between the user's search preferences and the category; and
      • a link-based profile 235—this profile identifies a plurality of links that are directly or indirectly related to the user's search preferences, with each link being given a weight indicating the relevance between the user's search preferences and the link.
  • [0041]
    In some embodiments, the user profile 230 includes only a subset of these profiles 231, 233, 235, for example just one or two of these profiles. In one embodiment, the user profile 230 includes a term-based profile 231 and a category-based profile 233, but not a link-based profile 235.
  • [0042]
    In one embodiment, a user profile is created and stored on a server (e.g., user profile server 108) associated with a search engine. The advantage of such deployment is that the user profile can be easily accessed by multiple computers, and that since the profile is stored on a server associated with (or part of) the search engine 104, it can be easily used by the search engine 104 to personalize the search results. In another embodiment, the user profile can be created and stored on the user's computer, sometimes called the client in a network environment. Creating and storing a user profile on a user's computer not only reduces the computational and storage cost for the search engine's servers, but also satisfies some users' privacy requirements. In yet another embodiment, the user profile may be created and updated on the client, but stored on a search engine server. Such embodiment combines some of the benefits illustrated in the other two embodiments. A disadvantage of this arrangement is that it may increase the network traffic between clients and the search engine servers. It is understood by a person of ordinary skill in the art that the user profiles of the present invention can be implemented using client computers, server computers, or both.
  • [0043]
    FIG. 3 illustrates an exemplary data structure, a term-based profile table 300, that may be used for storing term-based profiles for a plurality of users. Table 300 includes a plurality of records 310, each record corresponding to a user's term-based profile. A term-based profile record 310 includes a plurality of columns including a USER_ID column 320 and multiple columns of (TERM, WEIGHT) pairs 340. The USER_ID column stores a value that uniquely identifies a user or a group of users sharing the same set of (TERM, WEIGHT) pairs, and each (TERM, WEIGHT) pair 340 includes a term, typically 1-3 words long, that is usually important to the user or the group of users and a weight associated with the term that quantifies the importance of the term. In one embodiment, the term may be represented as one or more n-grams. An n-gram is defined as a sequence of n tokens, where the tokens may be words. For example, the phrase “search engine” is an n-gram of length 2, and the word “search” is an n-gram of length 1.
  • [0044]
    N-grams can be used to represent textual objects as vectors. This makes it possible to apply geometric, statistical and other mathematical techniques, which are well defined for vectors, but not for objects in general. In the present invention, n-grams can be used to define a similarity measure between two terms based on the application of a mathematical function to the vector representations of the terms.
  • [0045]
    The weight of a term is not necessarily a positive value. If a term has a negative weight, it may suggest that the user prefers that his search results should not include this term and the magnitude of the negative weight indicates the strength of the user's preference for avoiding this term in the search results. By way of example, for a group of surfing fans at Santa Cruz, Calif., the term-based profile may include terms like “surfing club”, “surfing event” and “Santa Cruz” with positive weights. The terms like “Internet surfing” or “web surfing” may also be included in the profile. However, these terms are more likely to receive a negative weight since they are irrelevant and confusing with the authentic preference of the users sharing this term-based profile.
  • [0046]
    A term-based profile itemizes a user's preference using specific terms, each term having certain weight. If a document matches a term in a user's term-based profile, i.e., its content includes exactly this term, the term's weight will be assigned to the document; however, if a document does not match a term exactly, it will not receive any weight associated with this term. Such a requirement of relevance between a document and a user profile sometimes may be less flexible when dealing with various scenarios in which a fuzzy relevance between a user's preference and a document exists. For example, if a user's term-based profile includes terms like “Mozilla” and “browser”, a document containing no such terms, but other terms like “Galeon” or “Opera” will not receive any weight because they do not match any existing term in the profile, even though they are actually Internet browsers. To address the need for matching a user's interests without exact term matching, a user's profile may include a category-based profile.
  • [0047]
    FIG. 4A illustrates a hierarchal category map 400 according to the Open Directory Project (http://dmoz.org/). Starting from the root level of map 400, documents are organized under several major topics, such as “Art”, “News”, “Sports”, etc. These major topics are often too broad to delineate a user's specific interest. Therefore, they are further divided into sub-topics that are more specific. For example, topic “Art” may comprise sub-topics like “Movie”, “Music” and “Literature” and the sub-topic “Music” may further comprise sub-sub-topics like “Lyrics”, “News” and “Reviews”. Note that each topic is associated with a unique CATEGORY_ID like 1.1 for “Art”, 1.4.2.3 for “Talk Show” and 1.6.1 for “Basketball”.
  • [0048]
    A user's specific interests may be associated with multiple categories at various levels, each of which may have a weight indicating the degree of relevance between the category and the user's interest. In one embodiment, a category-based profile may be implemented using a Hash table data structure as shown in FIG. 4B. A category-based profile table 450 includes a table that comprises a plurality of records 341-1, 342-2, . . . 342-n, each record including a USER_ID and a pointer pointing to another data structure, such as a table, e.g., one of the tables shown on the right side of FIG. 4B. This table may include two columns, a CATEGORY_ID column and a WEIGHT column. The CATEGORY_ID column contains a category's identification number as shown in FIG. 4A, suggesting that this category is relevant to the user's interests and the value in the WEIGHT column indicates the degree of relevance of the category to the user's interests.
  • [0049]
    A user profile based upon the category map 400 is a topic-oriented implementation. The items in a category-based profile can also be organized in other ways. In one embodiment, a user's preference can be categorized based on the formats of the documents identified by the user, such as HTML, plain text, PDF, Microsoft Word, etc. Different formats may have different weights. In another embodiment, a user's preference can be categorized according to the types of the identified documents, e.g., an organization's homepage, a person's homepage, a research paper, or a news group posting, each type having an associated weight. Another type category that can be used to characterize a user's search preferences is document origin, for instance the country associated with each document's host. In yet another embodiment, the above-identified category-based profiles may co-exist, with each one reflecting one aspect of a user's preferences.
  • [0050]
    Besides term-based and category-based profiles, another type of user profile is referred to as a link-based profile. As discussed above, the PageRank algorithm is based on the link structure that connects various documents over the Internet. A document that has more links pointing to it is often assigned a higher page rank and therefore attracts more attention from a search engine. Link information related to a document identified by a user can also be used to infer the user's preferences. In one embodiment, a list of preferred URLs are identified for a user by analyzing the frequency of his access to those URLs. Each preferred URL may be further weighted according to the time spent by the user and the user's scrolling activity at the URL, and/or other user activities (209, FIG. 2) when visiting the document at the URL. In another embodiment, a list of preferred hosts are identified for a user by analyzing the user's frequency of accessing web pages of different hosts. When two preferred URLs are related to the same host the weights of the two URLs may be combined to determine a weight for the host. In another embodiment, a list of preferred domains are identified for a user by analyzing the user's frequency of accessing web pages of different domains. For example, for finance.yahoo.com, the host is “finance.yahoo.com” while the domain is “yahoo.com”.
  • [0051]
    FIG. 5 illustrates a link-based profile using a Hash table data structure. A link-based profile table 500 includes a table 510 that includes a plurality of records 520, each record including a USER_ID and a pointer pointing to another data structure, such as table 510-1. Table 510-1 may include two columns, LINK_ID column 530 and WEIGHT column 540. The identification number stored in the LINK_ID column 530 may be associated with a preferred URL or host. The actual URL/host/domain may be stored in the table instead of the LINK_ID, however it is preferable to store the LINK_ID to save storage space.
  • [0052]
    A preferred list of URLs and/or hosts includes URLs and/or hosts that have been directly identified by the user. The preferred list of URLs and/or host may furthermore extend to URLs and/or hosts indirectly identified by using methods such as collaborative filtering or bibliometric analysis, which are known to persons of ordinary skill in the art. In one embodiment, the indirectly identified URLs and/or host include URLs or hosts that have links to/from the directly identified URLs and/or hosts. These indirectly identified URLs and/or hosts are weighted by the distance between them and the associated URLs or hosts that are directly identified by the user. For example, when a directly identified URL or host has a weight of 1, URLs or hosts that are one link away may have a weight of 0.5, URLs or hosts that are two links away may have a weight of 0.25, etc. This procedure can be further refined by reducing the weight of links that are not related to the topic of the original URL or host, e.g., links to copyright pages or web browser software that can be used to view the documents associated with the user selected URL or host. Irrelevant Links can be identified based on their context or their distribution. For example, copyright links often use specific terms (e.g., copyright or “All rights reserved” are commonly used terms in the anchor text of a copyright link); and links to a website from many unrelated websites may suggest that this website is not topically related (e.g., links to the Internet Explorer website are often included in unrelated websites). The indirect links can also be classified according to a set of topics and links with very different topics may be excluded or be assigned a low weight.
  • [0053]
    The three types of user profiles discussed above are generally complimentary to one another since different profiles delineate a user's interests and preferences from different vantage points. However, this does not mean that one type of user profile, e.g., category-based profile, is incapable of playing a role that is typically played by another type of user profile. By way of example, a preferred URL or host in a link-based profile is often associated with a specific topic, e.g., finance.yahoo.com is a URL focusing on financial news. Therefore, what is achieved by a link-based profile that comprises a list of preferred URLs or hosts to characterize a user's preference may also be achievable, at least in part, by a category-based profile that has a set of categories that cover the same topics covered by preferred URLs or hosts.
  • [0054]
    It is a non-trivial operation to construct various types of user profiles that can be stored in the data structures shown in FIGS. 3-5 based on the user information listed in FIG. 2. Given a document identified (e.g., viewed) by a user, different terms in the document may have different importance in revealing the topic of the document. Some terms, e.g., the document's title, may be extremely important, while other terms may have little importance. For example, many documents contain navigational links, copyright statements, disclaimers and other text that may not be related to the topic of the document. How to efficiently select appropriate documents, content from those documents and terms from within the content is a challenging topic in computational linguistics. Additionally, it is preferred to minimize the volume of user information processed, so as make the process of user profile construction computationally efficient. Skipping less important terms in a document helps in accurately matching a document with a user's interest.
  • [0055]
    Paragraph sampling (described below with reference to FIG. 6) is a procedure for automatically extracting content from a document that may be relevant to a user. An important observation behind this procedure is that less relevant content in a document, such as navigational links, copyright statements, disclaimer, etc., tend to be relatively short segments of text. In one embodiment, paragraph sampling looks for the paragraphs of greatest length in a document, processing the paragraphs in order of decreasing length until the length of a paragraph is below a predefined threshold. The paragraph sampling procedure optionally selects up to a certain maximum amount of content from each processed paragraph. If few paragraphs of suitable length are found in a document, the procedure falls back to extracting text from other parts of the document, such as anchor text and ALT tags.
  • [0056]
    FIG. 6 is a flowchart illustrating the major steps of paragraph sampling. Paragraph sampling begins with the step 610 of removing predefined items, such as comments, JavaScript and style sheets, etc., from a document. These items are removed because they are usually related to visual aspects of the document when rendered on a browser and are unlikely to be relevant to the document's topic. Following that, the procedure may select the first N words (or M sentences) at step 620 from each paragraph whose length is greater than a threshold value, MinParagraphLength, as sampled content. In one embodiment, the values of N and M are chosen to be 100 and 5, respectively. Other values may be used in other embodiments.
  • [0057]
    In order to reduce the computational and storage load associated with the paragraph sampling procedure, the procedure may impose a maximum limit, e.g., 1000 words, on the sampled content from each document. In one embodiment, the paragraph sampling procedure first organizes all the paragraphs in a document in length decreasing order, and then starts the sampling process with a paragraph of maximum length. It is noted that the beginning and end of a paragraph depend on the appearance of the paragraph in a browser, not on the presence of uninterrupted a text string in the HTML representation of the paragraph. For this reason, certain HTML commands, such as commands for inline links and for bold text, are ignored when determining paragraph boundaries. In some embodiments, the paragraph sampling procedure screens the first N words (or M sentences) so as to filter out those sentences including boilerplate terms like “Terms of Service” or “Best viewed”, because such sentences are usually deemed irrelevant to the document's topic.
  • [0058]
    Before sampling a paragraph whose length is above the threshold value, the procedure may stop sampling content from the document if the number of words in the sampled content has reached the maximum word limit. If the maximum word limit has not been reached after processing all paragraphs of length greater than the threshold, optional steps 630, 640, 650 and 670 are performed. In particular, the procedure adds the document title (630), the non-inline HREF links (640), the ALT tags (650) and the meta tags (670) to the sampled content until it reaches the maximum word limit.
  • [0059]
    Once the documents identified by a user have been scanned, the sampled content can be used for identifying a list of most important (or unimportant) terms through context analysis. Context analysis attempts to learn context terms that predict the most important (or unimportant) terms in a set of identified documents. Specifically, it looks for prefix patterns, postfix patterns, and a combination of both. For example, an expression “x's home page” may identify the term “x” as an important term for a user and therefore the postfix pattern “* home page” can be used to predict the location of an important term in a document, where the asterisk “*” represents any term that fits this postfix pattern. In general, the patterns identified by context analysis usually consist of m terms before an important (or unimportant) term and n terms after the important (or unimportant) term, where both m and n are greater than or equal to 0 and at least one of them is greater than 0. Typically, m and n are less than 5, and when non-zero are preferably between 1 and 3. Depending on its appearance frequency, a pattern may have an associated weight that indicates how important (or unimportant) the term recognized by the pattern is expected to be.
  • [0060]
    According to one embodiment of the present invention (FIG. 7A), context analysis has two distinct phases, a training phase 701 and an operational phase 703. The training phase 701 receives and utilizes a list of predefined important terms 712, an optional list of predefined unimportant terms 714, and a set of training documents (step 710). In some embodiments, the list of predefined unimportant terms is not used. The source of the lists 712, 714 is not critical. In some embodiments, these lists 712, 714 are generated by extracting words or terms from a set of documents (e.g., a set of several thousand web pages of high page rank) in accordance with a set of rules, and then editing them to remove terms that in the opinion of the editor do not belong in the lists. The source of the training documents is also not critical. In some embodiments, the training documents comprise a randomly or pseudo-randomly selected set of documents already known to the search engine. In other embodiments, the training documents are selected from a database of documents in the search engine in accordance with predefined criteria.
  • [0061]
    During the training phase 701, the training documents are processed (step 720), using the lists of predefined important and unimportant terms, so as to identify a plurality of context patterns (e.g., prefix patterns, postfix patterns, and prefix-postfix patterns) and to associate a weight with each identified context pattern. During the operational phase 703, the context patterns are applied to documents identified by the user (step 730) to identify a set of important terms (step 740) that characterize the user's specific interests and preferences. Learning and delineating a user's interests and preferences is usually an ongoing process. Therefore, the operational phase 703 may be repeated to update the set of important terms that have been captured previously. This may be done each time a user accesses a document, according to a predetermined schedule, at times determined in accordance with specified criteria, or otherwise from time to time. Similarly, the training phase 701 may also be repeated to discover new sets of context patterns and to recalibrate the weights associated with the identified context patterns.
  • [0062]
    Below is a segment of pseudo code that exemplifies the training phase:
  • [0000]
    For each document in the set {
      For each important term in the document {
       For m = 0 to MaxPrefix {
         For n = 0 to MaxPostfix {
          Extract the m words before the important
          term and the n words after the important
          term as s;
          Add 1 to ImportantContext(m,n,s);
         }
       }
      }
      For each unimportant term in the document {
       For m = 0 to MaxPrefix {
         For n = 0 to MaxPostfix {
          Extract the m words before the
          unimportant term and the n words after
          the unimportant term as s;
          Add 1 to UnimportantContext(m,n,s);
         }
       }
      }
    }
    For m = 0 to MaxPrefix {
      For n = 0 to MaxPostfix {
       For each value of s {
         Set the weight for s to a function of
         ImportantContext(m,n,s), and
         UnimportantContext(m,n,s);
       }
      }
    }
  • [0063]
    In the pseudo code above, the expression s refers to a prefix pattern (n=0), a postfix pattern (m=0) or a combination of both (m>0 & n>0). Each occurrence of a specific pattern is registered at one of the two multi-dimensional arrays, ImportantContext(m,n,s) or UnimportantContext(m,n,s). The weight of a prefix, postfix or combination pattern is set higher if this pattern identifies more important terms and fewer unimportant terms and vice versa. Note that it is possible that a same pattern may be associated with both important and unimportant terms. For example, the postfix expression “* operating system” may be used in the training documents 716 in conjunction with terms in the list of predefined important terms 712 and also used in conjunction with terms in the list of predefined unimportant terms 714. In this situation, the weight associated with the postfix pattern “* operating system” (represented by the expression Weight(1.0,“operating system”)) will take into account the number of times the postfix expression is used in conjunction with terms in the list of predefined important terms as well as the number of times the postfix expression is used in conjunction with terms in the list of predefined unimportant terms. One possible formula to determine the weight of a context pattern s is:
  • [0000]

    Weight(m,n,s)=Log(ImportantContext(m,n,s)+1)−Log(UnimportantContext(m,n,s)+1).
  • [0000]
    Other weight determination formulas may be used in other embodiments.
  • [0064]
    In the second phase of the context analysis process, the weighted context patterns are used to identify important terms in one or more documents identified by the user. Referring to FIG. 7B, in the first phase a computer system receives training data 750 and creates a set of context patterns 760, each context pattern having an associated weight. The computer system then applies the set of context patterns 760 to a document 780. In FIG. 7B, previously identified context patterns found within the document 780 are highlighted. Terms 790 associated with the context patterns are identified and each such term receives a weight based on the weights associated with the context patterns. For example, the term “Foobar” appears in the document twice, in association with two different patterns, the prefix pattern “Welcome to *” and the postfix pattern “* builds”, and the weight 1.2 assigned to “Foobar” is the sum of the two patterns' weights, 0.7 and 0.5. The other identified term “cars” has a weight of 0.8 because the matching prefix pattern “world's best *” has a weight of 0.8. In some embodiments the weight for each term is computed using a log transform, where the final weight is equal to log(initial weight+1). It is possible that the two terms “Foobar” and “cars” may not be in the training data 750 and may have never been encountered by the user before. Nevertheless, the context analysis method described above identifies these terms and adds them to the user's term-based profile. Thus, context analysis can be used to discover terms associated with a user's interests and preferences even when those terms are not included in a predefined database of terms.
  • [0065]
    As noted, the output of context analysis can be used directly in constructing a user's term-based profile. Additionally, it may be useful in building other types of user profiles, such as a user's category-based profile. For example, a set of weighted terms can be analyzed and classified into a plurality of categories covering different topics, and those categories can be added to a user's category-based profile.
  • [0066]
    After executing the context analysis on a set of documents identified by or for a user, the resulting set of terms and weights may occupy a larger amount of storage than allocated for each user's term-based profile. Also, the set of terms and corresponding weights may include some terms with weights much, much smaller than other terms within the set. Therefore, in some embodiments, at the conclusion of the context analysis, the set of terms and weights is pruned by removing terms having the lowest weights (A) so that the total amount of storage occupied by the term-based profile meets predefined limits, and/or (B) so as to remove terms whose weights are so low, or terms that correspond to older items, as defined by predefined criteria, that the terms are deemed to be not indicative of the user's search preferences and interests. In some embodiments, similar pruning criteria and techniques are also applied to the category-based profile and/or the link-based profile.
  • [0067]
    In some embodiments, a user's profile is updated each time the user performs a search and selects at least one document from the search results to download or view. In some embodiments, the search engine builds a list of documents identified by the user (e.g., by selecting the documents from search results) over time, and at predefined times (e.g., when the list reaches a predefined length, or a predefined amount of time has elapsed), performs a profile update. When performing an update, new profile data is generated, and the new profile data is merged with the previously generated profile data for the user. In some embodiments, the new profile data is assigned higher importance than the previously generated profile data, thereby enabling the system to quickly adjust a user's profile in accordance with changes in the user's search preferences and interests. For example, the weights of items in the previously generated profile data may be automatically scaled downward prior to merging with the new profile data. In one embodiment, there is a date associated with each item in the profile, and the information in the profile is weighted based on its age, with older items receiving a lower weight than when they were new. In other embodiments, the new profile data is not assigned high importance than the previously generated profile data.
  • [0068]
    The paragraph sampling and context analysis methods may be used independently or in combination. When used in combination, the output of the paragraph sampling is used as input to the context analysis method.
  • [0069]
    It is further noted that the above-described methods used for creating user profiles, e.g., paragraph sampling and context analysis, may be also leveraged for determining the relevance of a candidate document to a user's preference. Indeed, the primary mission of a search engine is to identify a series of documents that are most relevant to a user's preference based on the search queries submitted by the user as well as the user's user profile. FIG. 8 illustrates several exemplary data structures that can be used to store information about a document's relevance to a user profile from multiple perspectives. For each candidate document, each identified by a respective DOC_ID, term-based document information table 810 includes multiple pairs of terms and their weights, category-based document information table 830 includes a plurality of categories and associated weights, and link-based document information table 850 includes a set of links and corresponding weights.
  • [0070]
    The rightmost column of each of the three tables (810, 830 and 850) stores the rank (i.e., a computed score) of a document when the document is evaluated using one specific type of user profile. A user profile rank can be determined by combining the weights of the items associated with a document. For instance, a category-based or topic-based profile rank may be computed as follows. A user may prefer documents about science with a weight of 0.6, while he dislikes documents about business with a weight of −0.2. Thus, when a science document matches a search query, it will be weighted higher than a business document. In general, the document topic classification may not be exclusive. A candidate document may be classified as being a science document with probability of 0.8 and a business document with probability of 0.4. A link-based profile rank may be computed based on the relative weights allocated to a user's URL, host, domain, etc., preferences in the link-based profile. In one embodiment, term-based profile rank can be determined using known techniques, such as the term frequency-inverse document frequency (TF-IDF). The term frequency of a term is a function of the number of times the term appears in a document. The inverse document frequency is an inverse function of the number of documents in which the term appears within a collection of documents. For example, very common terms like “the” occur in many documents and consequently as assigned a relatively low inverse document frequency.
  • [0071]
    When a search engine generates search results in response to a search query, a candidate document D that satisfies the query is assigned a query score, QueryScore, in accordance with the search query. This query score is then modulated by document D's page rank, PageRank, to generate a generic score, GenericScore, that is expressed as
  • [0000]

    GenericScore=QueryScore*PageRank.
  • [0072]
    This generic score may not appropriately reflect document D's importance to a particular user U if the user's interests or preferences are dramatically different from that of the random surfer. The relevance of document D to user U can be accurately characterized by a set of profile ranks, based on the correlation between document D's content and user U's term-based profile, herein called the TermScore, the correlation between one or more categories associated with document D and user U's category-based profile, herein called the CategoryScore, and the correlation between the URL and/or host of document D and user U's link-based profile, herein called the LinkScore. Therefore, document D may be assigned a personalized rank that is a function of both the document's generic score and the user profile scores. In one embodiment, this personalized score can be expressed as:
  • [0000]

    PersonalizedScore=GenericScore*(TermScore+CategoryScore+LinkScore).
  • [0073]
    FIGS. 9A and 9B represent two embodiments, both implemented in a client-server network environment such as the network environment 100 shown in FIG. 1. In the embodiment shown in FIG. 9A, the search engine 104 receives a search query from a client 102 at step 910 that is submitted by a particular user. In response, the search engine 104 may optionally generate a query strategy at step 915 (e.g., the search query is normalized so as to be in proper form for further processing, and/or the search query may be modified in accordance with predefined criteria so as to automatically broaden or narrow the scope of the search query). At step 920, the search engine 104 submits the search query (or the query strategy, if one is generated) to the content server 106. The content server identifies a list of documents that match the search query at step 920, each document having a generic score that depends on the document's page rank and the search query. In general, all the three operations (steps 910, 915 and 920) are conducted by the search engine system 107, which is on the server side of the network environment 100. There are two options on where to implement the operations following these first three steps.
  • [0074]
    In some embodiments that employ a server-side implementation, the user's identification number is embedded in the search query. Based on the user's identification number, the user profile server 108 identifies the user's user profile at step 925. Starting from step 930, the user profile server 108 or the search engine 104 analyzes each document identified at step 920 to determine its relevance to the user's profile, creates a profile score for the identified document at step 935 and then assigns the document a personalized score that is a function of the document's generic and profile scores at step 940. At step 942, the user profile server 108 or the search engine 104 checks whether this the last one in the list of identified documents. If no, the system processes the next document in the list. Otherwise, the list of documents are re-ordered according to their personalized scores at step 945 and then sent to the corresponding client from which the user submitted the search query.
  • [0075]
    Embodiments using a client-side implementation are similar to the server-side implementation, except that after step 920, the identified documents are sent to the corresponding client from which the user submitted the query. This client stores the user's user profile and it is responsible for re-ordering the documents based upon the user profile. Therefore, this client-side implementation may reduce the server's workload. Further, since there is no privacy concern with the client-side implementation, a user may be more willing to provide private information to customize the search results. However, a significant limitation to the client-side implementation is that only a limited number of documents, e.g., the top 50 documents (as determined using the generic rank), may be sent to a client for re-ordering due to limited network bandwidth. In contrast, the server-side implementation may be able to apply a user's profile to a much larger number of documents, e.g., 1000, that match the search query. Therefore, the client-side implementation may deprive a user access to those documents having relatively low generic ranks, but significantly high personalized ranks.
  • [0076]
    FIG. 9B illustrates another embodiment. Unlike the embodiment depicted in FIG. 9A, where the search query is not personalized before submitting the search query to the search engine 104, a generic query strategy is adjusted (step 965) according to the user's user profile to create a personalized query strategy. For example, relevant terms from the user profile may be added to the search query with associated weights. The creation of the personalized query strategy can be performed either on the client side or on the server side of the system. This embodiment avoids the network bandwidth restriction facing the previous embodiment. Finally, the search engine 104 submits the personalized query strategy to the content server 106 (step 970), and therefore the search results returned by the content server have already been ordered by the documents' personalized ranks (step 975).
  • [0077]
    The profiles of a group of users with related interests may be combined together to form a group profile, or a single profile may be formed based on the documents identified by the users in the group. For instance, several family members may use the same computer to submit search queries to a search engine. If the computer is tagged with a single user identifier by the search engine, the “user” will be the entire family of users, and the user profile will be represent a combination or mixture of the search preferences of the various family members. An individual user in the group may optionally have a separate user profile that differentiates this user from other group members. In operation, the search results for a user in the group are ranked according to the group profile, or according to the group profile and the user's user profile when the user also has a separate user profile.
  • [0078]
    It is possible that a user may switch his interests so dramatically that his new interests and preferences bear little resemblance to his user profile, or a user may be temporarily interested in a new topic. In this case, personalized search results produced according to the embodiments depicted in FIGS. 9A and 9B may be less favorable than search results ranked in accordance with the generic ranks of the documents in the search results. Additionally, the search results provided to a user may not include new websites among the top listed documents because the user's profile tends to increase the weight of older websites which the user has visited (i.e., older websites from which the user has viewed or downloaded web pages) in the past.
  • [0079]
    To reduce the impact caused by a change in a user's preferences and interests, the personalized search results may be merged with the generic search results. In one embodiment, the generic search results and personalized search results are interleaved, with the odd positions (e.g., 1, 3, 5, etc.) of a search results list reserved for generic search results and the even positions (e.g., 2, 4, 6, etc.) reserved for personalized search results, or vice versa. Preferably, the items in the generic search results will not duplicate the items listed in the personalized search results, and vice versa. More generally, generic search results are intermixed or interleaved with personalized search results, so that the items in the search results presented to the user include both generic and personalized search results.
  • [0080]
    In another embodiment, the personalized ranks and generic ranks are further weighted by a user profile's confidence level. The confidence level takes into account factors such as how much information has been acquired about the user, how close the current search query matches the user's profile, how old the user profile is, etc. If only a very short history of the user is available, the user's profile may be assigned a correspondingly low confidence value. The final score of an identified document can be determined as:
  • [0000]

    FinalScore=ProfileScore*ProfileConfidence+GenericScore*(1−ProfileConfidence).
  • [0000]
    When intermixing generic and personalized results, the fraction of personalized results may be adjusted based on the profile confidence, for example using only one personalized result when the confidence is low.
  • [0081]
    Sometimes, multiple users may share a machine, e.g., in a public library. These users may have different interests and preferences. In one embodiment, a user may explicitly login to the service so the system knows his identity. Alternatively, different users can be automatically recognized based on the items they access or other characteristics of their access patterns. For example, different users may move the mouse in different ways, type differently, and use different applications and features of those applications. Based on a corpus of events on a client and/or server, it is possible to create a model for identifying users, and for then using that identification to select an appropriate “user” profile. In such circumstances, the “user” may actually be a group of people having somewhat similar computer usage patterns, interests and the like.
  • [0082]
    Referring to FIG. 10, a personalized search engine system 1000 typically includes one or more processing units (CPU's) 1002, one or more network or other communications interfaces 1010, memory 1012, and one or more communication buses 1014 for interconnecting these components. The system 1000 may optionally include a user interface 1004, for instance a display 1006 and a keyboard 1008. Memory 1012 may include high speed random access memory and may also include non-volatile memory, such as one or more magnetic disk storage devices. Memory 1012 may include mass storage that is remotely located from the central processing unit(s) 1002. The memory 1012 preferably stores:
      • an operating system 1016 that includes procedures for handling various basic system services and for performing hardware dependent tasks;
      • a network communication module 1018 that is used for connecting the system 1000 to other servers or computers via one or more communication networks (wired or wireless), such as the Internet, other wide area networks, local area networks, metropolitan area networks, and so on;
      • a system initialization module 1020 that initializes other modules and data structures stored in memory 1012 required for the appropriate operation of system 1000;
      • a search engine 1022 for processing a search query, identifying and ordering search results according to the search query and a user's profile;
      • a user profile engine 1030 for gathering and processing user information, such as the user information identified in FIG. 2, and creating and updating a user's user profile that characterizes the user's search preferences and interests; and
      • data structures 1040, 1060 and 1080 for storing a plurality of user profiles.
  • [0089]
    The search engine 1022 may further comprise:
      • a generic rank module (or instructions) 1024 for processing a search query submitted by a user, identifying a list of documents matching the query and assigning each identified document a generic rank without reference to user specific information;
      • a user profile rank module (or instructions) 1026 for correlating each of a plurality of documents identified by the generic rank module 1024 with the user's user profile and assigning the document a profile rank indicating the relevance of the document to the user's search preferences and interests; and
      • a rank mixing module (or instructions) 1028 for combining the generic rank and the profile rank of an identified document into a personalized rank and re-ordering the list of documents according to their personalized ranks.
        In some embodiments, these modules 1024, 1026, 1028 may be implemented within a single procedure or in a set of procedures that reside within a single software module.
  • [0093]
    The user profile engine 1030 may further comprise:
      • a user information collection module 1032 for collecting and assorting various user information listed in FIG. 2;
      • a document content extraction module 1034 for selecting and extracting content from the documents identified by the user, to identify content relevant to the user's interests, using techniques such as paragraph sampling (as discussed above); and
      • a context analysis module 1036 for analyzing the content extracted by the document extraction module 1034 so as to identify terms that characterize a user's search preferences.
  • [0097]
    Each data structure hosting a user profile may further comprise:
      • a data structure 1042, 1062 or 1082 for storing a term-based user profile;
      • a data structure 1044, 1064 or 1084 for storing a category-based user profile; and
      • a data structure 1046, 1066 or 1086 for storing a link-based user profile.
  • [0101]
    The foregoing description, for purpose of explanation, has been described with reference to specific embodiments. However, the illustrative discussions above are not intended to be exhaustive or to limit the invention to the precise forms disclosed. Many modifications and variations are possible in view of the above teachings. The embodiments were chosen and described in order to best explain the principles of the invention and its practical applications, to thereby enable others skilled in the art to best utilize the invention and various embodiments with various modifications as are suited to the particular use contemplated.

Claims (30)

1. A computer-implemented method of personalizing search results of a search engine, comprising:
at a search engine system having a one or more processors and memory storing programs executed by the one or more processors:
accessing a user profile for a user, wherein content of the user profile is generated from user information that includes information derived from anchor text contained in documents that link to documents accessed by the user;
receiving a search query from the user;
identifying a set of search result documents that match the search query;
assigning a generic score to each document of at least a subset of the set of search result documents;
assigning a personalized score to each document of the subset of search result documents in accordance with the generic score assigned to the document and the user profile;
ranking the subset of search result documents according to their respective personalized scores;
providing the ranked subset of search result documents to a client system associated with the user; and
updating the user profile based on a document selected by the user from the ranked subset of search result documents.
2. The method of claim 1, wherein the user information is derived from a first set of documents that includes: documents identified by search results from the search engine, documents accessed by the user, documents linked to the documents identified by search results from the search engine, and documents linked to the documents accessed by the user.
3. The method of claim 1, including updating the user profile by:
updating a term-based profile of the user profile by identifying a set of terms from a document in the first set of documents, and adding information about the identified set of terms to the term-based profile.
4. The method of claim 3, wherein a term in the term-based profile is an expression comprising at least one word and a weight.
5. The method of claim 4, wherein the weight is a weight associated with occurrences of the term in the first set of documents.
6. The method of claim 4, wherein the weight of a term depends at least partially on the term's term frequency and inverse document frequency in said first set of documents.
7. The method of claim 1, wherein the updating includes analyzing links within a document in the first set of documents and adding information derived from the analyzed links to the user profile.
8. The method of claim 7, wherein the information derived from the analyzed links that is added to the user profile is added to a link-based profile and includes information about URLs or portions of URLs.
9. The method of claim 8, wherein the link-based profile of the user profile comprises:
a plurality of URLs and a weight associated with each URL, wherein the weight is based on one or more factors selected from the group consisting of frequency with which the user visits the URL, time the user has spent viewing a document associated with the URL and quantity of the user's scrolling activity at the document; and
a plurality of hosts and a weight associated with each host, wherein the weight is based on frequency of the user's visits to the host.
10. The method of claim 9, wherein the URLs further include URLs that have not been visited by the user, but are related to the URLs that have been visited by the user and the weight of an unvisited URL depends on its distance to at least one related URLs that have been visited.
11. The method of claim 1, wherein the updating includes updating a category-based profile of the user profile by classifying a document in the first set of documents into a plurality of categories, and adding information about the plurality of categories to the category-based profile.
12. The method of claim 11, wherein a category in the category-based profile characterizes at least one aspect of documents in the category and the category is associated with a weight indicative of the category's importance relative to other categories.
13. The method of claim 12, wherein the at least one aspect of the documents in the category is selected from the group consisting of: document format, document type, document topic and document origin.
14. A computer-implemented method of personalizing search results of a search engine, comprising:
creating a plurality of user profiles for a plurality of users, each user profile including at least a user's identification number and information derived from documents visited by the user, including information derived from anchor text contained in documents that link to the documents visited by the user;
receiving a search query from a user of the plurality of users, the search query including at least one query term and the user's identification number;
retrieving a user profile that matches the user's identification number;
generating a personalized query strategy from the search query and the user profile;
selecting a personalized set of documents from the Internet according to the personalized query strategy, each document having a generic ranking score based at least in part on the relevance of the document to the search query;
assigning to each document in the set a personalized ranking score based at least in part on the user profile and the document's generic ranking score;
ranking the set of documents according to their generic and personalized ranking scores;
providing the ranked set of search result documents to a client system associated with the user; and
updating the user profile of the user based on a document selected by the user from the set of search result documents.
15. The method of claim 14, wherein creating a user's user profile further comprises:
creating a term-based profile by extracting a set of terms from documents visited by the user and associating a weight with each extracted term; and
creating a category-based profile by determining a plurality of categories associated with documents visited by the user and associating a weight with each determined category.
16. The method of claim 14, wherein creating a user's user profile further comprises:
creating a link-based profile by analyzing links in documents visited by the user and associating weights with the link.
17. The method of claim 14, wherein the user profile for a particular user includes demographic and geographic information provided by the user.
18. The method of claim 14, wherein the ranked set of documents comprises two subsets of documents, one subset of documents ordered by their generic ranking scores and the other subset of documents ordered by personalized ranking scores.
19. A search engine system, comprising:
one or more central processing units for executing programs;
an interface for receiving information; and
a search engine module executable by the one or more central processing units, the module comprising:
instructions for accessing a user profile for a user, wherein content of the user profile is generated from user information that includes information derived from anchor text contained in documents that link to documents accessed by the user;
instructions for receiving a search query from a user;
instructions for identifying a set of search result documents that match the search query;
instructions for assigning a generic score to each document of at least a plurality of the search result documents;
instructions for assigning personalized scores to each document of the plurality of search result documents in accordance with the generic score assigned to the document and the user's user profile;
instructions for ranking at least the plurality of the search result documents according to personalized scores;
instructions for providing the ranked set of search result documents to a client system associated with the user; and
instructions for updating the user profile based on a document selected by the user from the set of search result documents.
20. The system of claim 19, wherein the user information is derived from a first set of documents that includes: documents identified by search results from the search engine, documents accessed by the user, documents linked to the documents identified by search results from the search engine, and documents linked to the documents accessed by the user.
21. The system of claim 19, wherein the information derived from the analyzed links that is added to the user profile is added to a link-based profile and includes information about URLs or portions of URLs.
22. The system of claim 21, wherein the link-based profile comprises:
a plurality of URLs and a weight associated with each URL, wherein the weight is based on one or more factors selected from the group consisting of frequency with which the user visits the URL, time the user has spent viewing a document associated with the URL and quantity of the user's scrolling activity at the document; and
a plurality of hosts and a weight associated with each host, wherein the weight is based on frequency of the user's visits to the host.
23. The system of claim 22, wherein the URLs further include URLs that have not been visited by the user, but are related to the URLs that have been visited by the user and the weight of an unvisited URL depends on its distance to at least one related URLs that have been visited.
24. The system of claim 19, further including:
instructions for updating a term-based profile by identifying a set of terms from a document in the set of documents, and adding information about the identified set of terms to the term-based profile; and
instructions for updating a category-based profile by classifying the document into a plurality of categories, and adding information about the plurality of categories to the category-based profile.
25. The system of claim 24, wherein a term in the term-based profile is an expression comprising at least one word and a weight.
26. The system of claim 25, wherein the weight is a weight associated with occurrences of the term in the set of documents.
27. The system of claim 25, wherein the weight of a term depends at least partially on the term's term frequency and inverse document frequency in said set of documents.
28. A computer readable storage medium storing one or more programs for execution by one or more processors, the one or more programs comprising:
instructions for accessing a user profile for a user, wherein content of the user profile is generated from user information that includes information derived from anchor text contained in documents that link to documents accessed by the user;
instructions for receiving a search query from a user;
instructions for identifying a set of search result documents that match the search query;
instructions for assigning a generic score to each document of at least a plurality of the search result documents;
instructions for assigning personalized scores to each document of the plurality of search result documents in accordance with the generic score assigned to the document and the user's user profile;
instructions for ranking at least the plurality of the search result documents according to their personalized scores;
instructions for providing the ranked set of search result documents to a client system associated with the user; and
instructions for updating the user profile based on a document selected by the user from the set of search result documents.
29. A computer-implemented method of personalizing search results of a search engine, comprising:
at a search engine system having a one or more processors and memory storing programs executed by the one or more processors:
identifying a set of documents accessed by a user;
generating a user profile for the user that includes terms selected by sampling the identified set of documents, wherein sampling a document includes:
excluding a set of predefined terms;
excluding paragraphs whose lengths are less than a predefined minimum length; and
limiting the number of terms selected from each paragraph to a predefined maximum number;
receiving a search query from the user;
identifying a set of search result documents that match the search query;
assigning a generic score to each document of at least a subset of the set of search result documents;
assigning a personalized score to each document of the subset of search result documents in accordance with the generic score assigned to the document and the user profile;
ranking the subset of search result documents according to their respective personalized scores;
providing the ranked subset of search result documents to a client system associated with the user; and
updating the user profile based on a document selected by the user from the ranked subset of search result documents.
30. A computer-implemented method of personalizing search results of a search engine, comprising:
at a search engine system having a one or more processors and memory storing programs executed by the one or more processors:
identifying a plurality of context patterns from a predefined set of documents, wherein each respective context pattern comprises a respective variable term and one or more respective fixed terms, and the respective variable term together with the respective fixed terms are in an identified ordered sequence;
generating a user profile for a user, wherein content of the user profile includes terms identified by applying the plurality of context patterns to documents accessed by the user;
receiving a search query from the user;
identifying a set of search result documents that match the search query;
assigning a generic score to each document of at least a subset of the set of search result documents;
assigning a personalized score to each document of the subset of search result documents in accordance with the generic score assigned to the document and the user profile;
ranking the subset of search result documents according to their respective personalized scores;
providing the ranked subset of search result documents to a client system associated with the user; and
updating the user profile based on a document selected by the user from the ranked subset of search result documents.
US12778869 2003-09-30 2010-05-12 Personalization of Web Search Results Using Term, Category, and Link-Based User Profiles Abandoned US20100228715A1 (en)

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US13735953 US9298777B2 (en) 2003-09-30 2013-01-07 Personalization of web search results using term, category, and link-based user profiles
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Cited By (44)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20080109422A1 (en) * 2006-11-02 2008-05-08 Yahoo! Inc. Personalized search
US20090030923A1 (en) * 2007-07-26 2009-01-29 International Business Machines Corporation Identification of shared resources
US20090119248A1 (en) * 2007-11-02 2009-05-07 Neelakantan Sundaresan Search based on diversity
US20090216750A1 (en) * 2008-02-25 2009-08-27 Michael Sandoval Electronic profile development, storage, use, and systems therefor
US20090216563A1 (en) * 2008-02-25 2009-08-27 Michael Sandoval Electronic profile development, storage, use and systems for taking action based thereon
US20090216749A1 (en) * 2007-11-28 2009-08-27 Blame Canada Holdings Inc. Identity based content filtering
US20090292688A1 (en) * 2008-05-23 2009-11-26 Yahoo! Inc. Ordering relevant content by time for determining top picks
US20090327960A1 (en) * 2008-06-26 2009-12-31 Microsoft Corporation Ordered Multiple Selection User Interface
US20110029515A1 (en) * 2009-07-31 2011-02-03 Scholz Martin B Method and system for providing website content
US20110035388A1 (en) * 2008-01-02 2011-02-10 Samsung Electronics Co., Ltd. Method and apparatus for recommending information using a hybrid algorithm
US8095534B1 (en) 2011-03-14 2012-01-10 Vizibility Inc. Selection and sharing of verified search results
US20120072460A1 (en) * 2010-09-17 2012-03-22 International Business Machines Corporation User accessibility to data analytics
US20120185472A1 (en) * 2011-01-13 2012-07-19 International Business Machines Corporation Relevancy Ranking of Search Results in a Network Based Upon a User's Computer-Related Activities
WO2012173903A2 (en) * 2011-06-16 2012-12-20 Microsoft Corporation Search results based on user and result profiles
US8370350B2 (en) 2010-09-03 2013-02-05 International Business Machines Corporation User accessibility to resources enabled through adaptive technology
US8429182B2 (en) 2010-10-13 2013-04-23 International Business Machines Corporation Populating a task directed community in a complex heterogeneous environment based on non-linear attributes of a paradigmatic cohort member
US8560365B2 (en) 2010-06-08 2013-10-15 International Business Machines Corporation Probabilistic optimization of resource discovery, reservation and assignment
US20130290339A1 (en) * 2012-04-27 2013-10-31 Yahoo! Inc. User modeling for personalized generalized content recommendations
US20130297590A1 (en) * 2012-04-09 2013-11-07 Eli Zukovsky Detecting and presenting information to a user based on relevancy to the user's personal interest
US20140032517A1 (en) * 2012-07-25 2014-01-30 Ebay Inc. System and methods to configure a profile to rank search results
US20140095496A1 (en) * 2011-06-30 2014-04-03 Nokia Corporation Method and apparatus for providing user-corrected search results
US8700544B2 (en) 2011-06-17 2014-04-15 Microsoft Corporation Functionality for personalizing search results
US20140195528A1 (en) * 2013-01-04 2014-07-10 International Business Machines Corporation System and method for reflective searching of previous search results
US20140195244A1 (en) * 2013-01-07 2014-07-10 Samsung Electronics Co., Ltd. Display apparatus and method of controlling display apparatus
WO2014185742A1 (en) * 2013-05-16 2014-11-20 Samsung Electronics Co., Ltd. Computing system with privacy mechanism and method of operation thereof
US8954423B2 (en) 2011-09-06 2015-02-10 Microsoft Technology Licensing, Llc Using reading levels in responding to requests
US8968197B2 (en) 2010-09-03 2015-03-03 International Business Machines Corporation Directing a user to a medical resource
US8984647B2 (en) 2010-05-06 2015-03-17 Atigeo Llc Systems, methods, and computer readable media for security in profile utilizing systems
US20150161092A1 (en) * 2013-12-05 2015-06-11 Lenovo (Singapore) Pte. Ltd. Prioritizing smart tag creation
US9158768B2 (en) 2012-07-25 2015-10-13 Paypal, Inc. System and methods to configure a query language using an operator dictionary
US20150293913A1 (en) * 2014-04-10 2015-10-15 Ca, Inc. Content augmentation based on a content collection's membership
US9253282B2 (en) 2011-10-18 2016-02-02 Qualcomm Incorporated Method and apparatus for generating, using, or updating an enriched user profile
US9275149B2 (en) 2012-08-22 2016-03-01 International Business Machines Corporation Utilizing social network relevancy as a factor in ranking search results
US9443211B2 (en) 2010-10-13 2016-09-13 International Business Machines Corporation Describing a paradigmatic member of a task directed community in a complex heterogeneous environment based on non-linear attributes
US20160350797A1 (en) * 2008-03-31 2016-12-01 Yahoo! Inc. Ranking advertisements with pseudo-relevance feedback and translation models
US9639611B2 (en) * 2010-06-11 2017-05-02 Doat Media Ltd. System and method for providing suitable web addresses to a user device
US9646271B2 (en) 2010-08-06 2017-05-09 International Business Machines Corporation Generating candidate inclusion/exclusion cohorts for a multiply constrained group
US9665647B2 (en) 2010-06-11 2017-05-30 Doat Media Ltd. System and method for indexing mobile applications
US9727545B1 (en) * 2013-12-04 2017-08-08 Google Inc. Selecting textual representations for entity attribute values
US9785883B2 (en) 2012-04-27 2017-10-10 Excalibur Ip, Llc Avatars for use with personalized generalized content recommendations
US9836545B2 (en) 2012-04-27 2017-12-05 Yahoo Holdings, Inc. Systems and methods for personalized generalized content recommendations
US9846699B2 (en) 2010-06-11 2017-12-19 Doat Media Ltd. System and methods thereof for dynamically updating the contents of a folder on a device
US9858342B2 (en) 2011-03-28 2018-01-02 Doat Media Ltd. Method and system for searching for applications respective of a connectivity mode of a user device
US9886674B2 (en) 2016-07-25 2018-02-06 International Business Machines Corporation Describing a paradigmatic member of a task directed community in a complex heterogeneous environment based on non-linear attributes

Families Citing this family (372)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US7302429B1 (en) * 1999-04-11 2007-11-27 William Paul Wanker Customizable electronic commerce comparison system and method
US7219073B1 (en) 1999-08-03 2007-05-15 Brandnamestores.Com Method for extracting information utilizing a user-context-based search engine
US20020002563A1 (en) * 1999-08-23 2002-01-03 Mary M. Bendik Document management systems and methods
US6434747B1 (en) * 2000-01-19 2002-08-13 Individual Network, Inc. Method and system for providing a customized media list
US8813123B2 (en) * 2000-01-19 2014-08-19 Interad Technologies, Llc Content with customized advertisement
US9710852B1 (en) 2002-05-30 2017-07-18 Consumerinfo.Com, Inc. Credit report timeline user interface
US9400589B1 (en) 2002-05-30 2016-07-26 Consumerinfo.Com, Inc. Circular rotational interface for display of consumer credit information
US20040111423A1 (en) * 2002-07-13 2004-06-10 John Irving Method and system for secure, community profile generation and access via a communication system
US8838622B2 (en) 2002-07-13 2014-09-16 Cricket Media, Inc. Method and system for monitoring and filtering data transmission
US20040103118A1 (en) * 2002-07-13 2004-05-27 John Irving Method and system for multi-level monitoring and filtering of electronic transmissions
US20040122692A1 (en) * 2002-07-13 2004-06-24 John Irving Method and system for interactive, multi-user electronic data transmission in a multi-level monitored and filtered system
US20040103122A1 (en) * 2002-07-13 2004-05-27 John Irving Method and system for filtered web browsing in a multi-level monitored and filtered system
US8713025B2 (en) 2005-03-31 2014-04-29 Square Halt Solutions, Limited Liability Company Complete context search system
US20080027769A1 (en) * 2002-09-09 2008-01-31 Jeff Scott Eder Knowledge based performance management system
US9443268B1 (en) 2013-08-16 2016-09-13 Consumerinfo.Com, Inc. Bill payment and reporting
CA2468481A1 (en) * 2003-05-26 2004-11-26 John T. Forbis Multi-position rail for a barrier
US20050278362A1 (en) * 2003-08-12 2005-12-15 Maren Alianna J Knowledge discovery system
US20050038699A1 (en) * 2003-08-12 2005-02-17 Lillibridge Mark David System and method for targeted advertising via commitment
US7333997B2 (en) * 2003-08-12 2008-02-19 Viziant Corporation Knowledge discovery method with utility functions and feedback loops
US7693827B2 (en) * 2003-09-30 2010-04-06 Google Inc. Personalization of placed content ordering in search results
US8321278B2 (en) 2003-09-30 2012-11-27 Google Inc. Targeted advertisements based on user profiles and page profile
US7165119B2 (en) * 2003-10-14 2007-01-16 America Online, Inc. Search enhancement system and method having rankings, explicitly specified by the user, based upon applicability and validity of search parameters in regard to a subject matter
US7640232B2 (en) * 2003-10-14 2009-12-29 Aol Llc Search enhancement system with information from a selected source
US9288000B2 (en) 2003-12-17 2016-03-15 International Business Machines Corporation Monitoring a communication and retrieving information relevant to the communication
KR20060125842A (en) * 2004-01-20 2006-12-06 코닌클리케 필립스 일렉트로닉스 엔.브이. Automatic creation of e-books
US8010459B2 (en) 2004-01-21 2011-08-30 Google Inc. Methods and systems for rating associated members in a social network
US8005835B2 (en) * 2004-03-15 2011-08-23 Yahoo! Inc. Search systems and methods with integration of aggregate user annotations
US7584221B2 (en) * 2004-03-18 2009-09-01 Microsoft Corporation Field weighting in text searching
US7716223B2 (en) 2004-03-29 2010-05-11 Google Inc. Variable personalization of search results in a search engine
US20050216446A1 (en) * 2004-03-29 2005-09-29 Hall Karl E Technical process to deliver pre-populated search suggestions using the intelli-match search methodology
US9626437B2 (en) * 2004-06-10 2017-04-18 International Business Machines Corporation Search scheduling and delivery tool for scheduling a search using a search framework profile
US7836411B2 (en) * 2004-06-10 2010-11-16 International Business Machines Corporation Search framework metadata
US7827175B2 (en) * 2004-06-10 2010-11-02 International Business Machines Corporation Framework reactive search facility
WO2006007194B1 (en) * 2004-06-25 2006-03-02 Personasearch Inc Dynamic search processor
US7774340B2 (en) * 2004-06-30 2010-08-10 Microsoft Corporation Method and system for calculating document importance using document classifications
JP4251634B2 (en) * 2004-06-30 2009-04-08 株式会社東芝 Multimedia data reproducing apparatus and multimedia data reproduction method
US7562068B2 (en) * 2004-06-30 2009-07-14 Microsoft Corporation System and method for ranking search results based on tracked user preferences
US7702618B1 (en) 2004-07-26 2010-04-20 Google Inc. Information retrieval system for archiving multiple document versions
US7580929B2 (en) * 2004-07-26 2009-08-25 Google Inc. Phrase-based personalization of searches in an information retrieval system
US7711679B2 (en) 2004-07-26 2010-05-04 Google Inc. Phrase-based detection of duplicate documents in an information retrieval system
US7584175B2 (en) 2004-07-26 2009-09-01 Google Inc. Phrase-based generation of document descriptions
US7599914B2 (en) * 2004-07-26 2009-10-06 Google Inc. Phrase-based searching in an information retrieval system
US7536408B2 (en) 2004-07-26 2009-05-19 Google Inc. Phrase-based indexing in an information retrieval system
US7567959B2 (en) 2004-07-26 2009-07-28 Google Inc. Multiple index based information retrieval system
US7580921B2 (en) 2004-07-26 2009-08-25 Google Inc. Phrase identification in an information retrieval system
US7199571B2 (en) * 2004-07-27 2007-04-03 Optisense Network, Inc. Probe apparatus for use in a separable connector, and systems including same
US9053754B2 (en) * 2004-07-28 2015-06-09 Microsoft Technology Licensing, Llc Thumbnail generation and presentation for recorded TV programs
WO2006011819A1 (en) * 2004-07-30 2006-02-02 Eurekster, Inc. Adaptive search engine
US20060036598A1 (en) * 2004-08-09 2006-02-16 Jie Wu Computerized method for ranking linked information items in distributed sources
US20060047643A1 (en) * 2004-08-31 2006-03-02 Chirag Chaman Method and system for a personalized search engine
US7493301B2 (en) * 2004-09-10 2009-02-17 Suggestica, Inc. Creating and sharing collections of links for conducting a search directed by a hierarchy-free set of topics, and a user interface therefor
CN101073077A (en) * 2004-09-10 2007-11-14 色杰斯提卡股份有限公司 User creating and rating of attachments for conducting a search directed by a hierarchy-free set of topics, and a user interface therefor
US7321889B2 (en) * 2004-09-10 2008-01-22 Suggestica, Inc. Authoring and managing personalized searchable link collections
US8666816B1 (en) 2004-09-14 2014-03-04 Google Inc. Method and system for access point customization
US20060058019A1 (en) * 2004-09-15 2006-03-16 Chan Wesley T Method and system for dynamically modifying the appearance of browser screens on a client device
US20060074864A1 (en) * 2004-09-24 2006-04-06 Microsoft Corporation System and method for controlling ranking of pages returned by a search engine
US7606793B2 (en) * 2004-09-27 2009-10-20 Microsoft Corporation System and method for scoping searches using index keys
US7761448B2 (en) * 2004-09-30 2010-07-20 Microsoft Corporation System and method for ranking search results using click distance
US8635216B1 (en) * 2004-09-30 2014-01-21 Avaya Inc. Enhancing network information retrieval according to a user search profile
US7827181B2 (en) * 2004-09-30 2010-11-02 Microsoft Corporation Click distance determination
US7739277B2 (en) * 2004-09-30 2010-06-15 Microsoft Corporation System and method for incorporating anchor text into ranking search results
US20060074883A1 (en) * 2004-10-05 2006-04-06 Microsoft Corporation Systems, methods, and interfaces for providing personalized search and information access
WO2006042265A3 (en) 2004-10-11 2007-02-01 Nextumi Inc System and method for facilitating network connectivity based on user characteristics
US7904337B2 (en) * 2004-10-19 2011-03-08 Steve Morsa Match engine marketing
US20060085401A1 (en) * 2004-10-20 2006-04-20 Microsoft Corporation Analyzing operational and other data from search system or the like
US8930358B2 (en) * 2004-10-26 2015-01-06 Yahoo! Inc. System and method for presenting search results
US20060167942A1 (en) * 2004-10-27 2006-07-27 Lucas Scott G Enhanced client relationship management systems and methods with a recommendation engine
US7779001B2 (en) * 2004-10-29 2010-08-17 Microsoft Corporation Web page ranking with hierarchical considerations
WO2006050245A3 (en) * 2004-11-02 2007-10-25 Stanley Campbel System and method for predictive analysis and predictive analysis markup language
US7426499B2 (en) * 2004-11-08 2008-09-16 Asset Trust, Inc. Search ranking system
US20060101012A1 (en) * 2004-11-11 2006-05-11 Chad Carson Search system presenting active abstracts including linked terms
US7606794B2 (en) * 2004-11-11 2009-10-20 Yahoo! Inc. Active Abstracts
US8185514B1 (en) * 2004-11-16 2012-05-22 Topix Llc User-interface feature and technique for providing users of a network site links that have been determined to be of interest to the user
US8874570B1 (en) 2004-11-30 2014-10-28 Google Inc. Search boost vector based on co-visitation information
US7739270B2 (en) * 2004-12-07 2010-06-15 Microsoft Corporation Entity-specific tuned searching
US7716198B2 (en) * 2004-12-21 2010-05-11 Microsoft Corporation Ranking search results using feature extraction
US7469276B2 (en) * 2004-12-27 2008-12-23 International Business Machines Corporation Service offering for the delivery of information with continuing improvement
US8364670B2 (en) * 2004-12-28 2013-01-29 Dt Labs, Llc System, method and apparatus for electronically searching for an item
US8538970B1 (en) 2004-12-30 2013-09-17 Google Inc. Personalizing search results
US20070189544A1 (en) 2005-01-15 2007-08-16 Outland Research, Llc Ambient sound responsive media player
US7562117B2 (en) * 2005-09-09 2009-07-14 Outland Research, Llc System, method and computer program product for collaborative broadcast media
US20060161621A1 (en) * 2005-01-15 2006-07-20 Outland Research, Llc System, method and computer program product for collaboration and synchronization of media content on a plurality of media players
US7489979B2 (en) * 2005-01-27 2009-02-10 Outland Research, Llc System, method and computer program product for rejecting or deferring the playing of a media file retrieved by an automated process
US7542816B2 (en) * 2005-01-27 2009-06-02 Outland Research, Llc System, method and computer program product for automatically selecting, suggesting and playing music media files
US20070276870A1 (en) * 2005-01-27 2007-11-29 Outland Research, Llc Method and apparatus for intelligent media selection using age and/or gender
US7586032B2 (en) * 2005-10-07 2009-09-08 Outland Research, Llc Shake responsive portable media player
US20060173828A1 (en) * 2005-02-01 2006-08-03 Outland Research, Llc Methods and apparatus for using personal background data to improve the organization of documents retrieved in response to a search query
US20060173556A1 (en) * 2005-02-01 2006-08-03 Outland Research,. Llc Methods and apparatus for using user gender and/or age group to improve the organization of documents retrieved in response to a search query
US20060179044A1 (en) * 2005-02-04 2006-08-10 Outland Research, Llc Methods and apparatus for using life-context of a user to improve the organization of documents retrieved in response to a search query from that user
US8176101B2 (en) 2006-02-07 2012-05-08 Google Inc. Collaborative rejection of media for physical establishments
US9092523B2 (en) * 2005-02-28 2015-07-28 Search Engine Technologies, Llc Methods of and systems for searching by incorporating user-entered information
US8131736B1 (en) * 2005-03-01 2012-03-06 Google Inc. System and method for navigating documents
US7792833B2 (en) * 2005-03-03 2010-09-07 Microsoft Corporation Ranking search results using language types
US20060200460A1 (en) * 2005-03-03 2006-09-07 Microsoft Corporation System and method for ranking search results using file types
GB0505007D0 (en) * 2005-03-11 2005-04-20 Alamy Ltd Ranking of images in the results of a search
KR101374651B1 (en) 2005-03-18 2014-03-17 써치 엔진 테크놀로지스, 엘엘씨 Search engine that applies feedback from users to improve search results
US20060253210A1 (en) * 2005-03-26 2006-11-09 Outland Research, Llc Intelligent Pace-Setting Portable Media Player
US8838588B2 (en) * 2005-03-30 2014-09-16 International Business Machines Corporation System and method for dynamically tracking user interests based on personal information
US7783631B2 (en) 2005-03-31 2010-08-24 Google Inc. Systems and methods for managing multiple user accounts
US20060223637A1 (en) * 2005-03-31 2006-10-05 Outland Research, Llc Video game system combining gaming simulation with remote robot control and remote robot feedback
US7747632B2 (en) 2005-03-31 2010-06-29 Google Inc. Systems and methods for providing subscription-based personalization
US9256685B2 (en) 2005-03-31 2016-02-09 Google Inc. Systems and methods for modifying search results based on a user's history
US7694212B2 (en) 2005-03-31 2010-04-06 Google Inc. Systems and methods for providing a graphical display of search activity
US20060223635A1 (en) * 2005-04-04 2006-10-05 Outland Research method and apparatus for an on-screen/off-screen first person gaming experience
US8412698B1 (en) 2005-04-07 2013-04-02 Yahoo! Inc. Customizable filters for personalized search
US7421419B2 (en) * 2005-04-12 2008-09-02 Viziant Corporation System and method for evidence accumulation and hypothesis generation
US20060253572A1 (en) * 2005-04-13 2006-11-09 Osmani Gomez Method and system for management of an electronic mentoring program
US7599916B2 (en) * 2005-04-20 2009-10-06 Microsoft Corporation System and method for personalized search
US20070210937A1 (en) * 2005-04-21 2007-09-13 Microsoft Corporation Dynamic rendering of map information
US8843309B2 (en) * 2005-04-21 2014-09-23 Microsoft Corporation Virtual earth mapping
US20060242130A1 (en) * 2005-04-23 2006-10-26 Clenova, Llc Information retrieval using conjunctive search and link discovery
US8606781B2 (en) * 2005-04-29 2013-12-10 Palo Alto Research Center Incorporated Systems and methods for personalized search
US7647312B2 (en) * 2005-05-12 2010-01-12 Microsoft Corporation System and method for automatic generation of suggested inline search terms
US20060256007A1 (en) * 2005-05-13 2006-11-16 Outland Research, Llc Triangulation method and apparatus for targeting and accessing spatially associated information
US20060259574A1 (en) * 2005-05-13 2006-11-16 Outland Research, Llc Method and apparatus for accessing spatially associated information
US20060256008A1 (en) * 2005-05-13 2006-11-16 Outland Research, Llc Pointing interface for person-to-person information exchange
US20060229058A1 (en) * 2005-10-29 2006-10-12 Outland Research Real-time person-to-person communication using geospatial addressing
US20060271286A1 (en) * 2005-05-27 2006-11-30 Outland Research, Llc Image-enhanced vehicle navigation systems and methods
US20070150188A1 (en) * 2005-05-27 2007-06-28 Outland Research, Llc First-person video-based travel planning system
US7962462B1 (en) 2005-05-31 2011-06-14 Google Inc. Deriving and using document and site quality signals from search query streams
US8103659B1 (en) * 2005-06-06 2012-01-24 A9.Com, Inc. Perspective-based item navigation
US20060186197A1 (en) * 2005-06-16 2006-08-24 Outland Research Method and apparatus for wireless customer interaction with the attendants working in a restaurant
WO2007002820A3 (en) * 2005-06-28 2007-05-18 Yahoo Inc Search engine with augmented relevance ranking by community participation
US20080005064A1 (en) * 2005-06-28 2008-01-03 Yahoo! Inc. Apparatus and method for content annotation and conditional annotation retrieval in a search context
US7472119B2 (en) * 2005-06-30 2008-12-30 Microsoft Corporation Prioritizing search results by client search satisfaction
US20070011049A1 (en) * 2005-07-09 2007-01-11 Eder Jeffrey S Intelligent, personalized commerce chain
US9715542B2 (en) * 2005-08-03 2017-07-25 Search Engine Technologies, Llc Systems for and methods of finding relevant documents by analyzing tags
US7599917B2 (en) * 2005-08-15 2009-10-06 Microsoft Corporation Ranking search results using biased click distance
US7966395B1 (en) 2005-08-23 2011-06-21 Amazon Technologies, Inc. System and method for indicating interest of online content
US20070198486A1 (en) * 2005-08-29 2007-08-23 Daniel Abrams Internet search engine with browser tools
US20070156720A1 (en) * 2005-08-31 2007-07-05 Eagleforce Associates System for hypothesis generation
US20070051503A1 (en) * 2005-09-08 2007-03-08 Grajzl Harold A Corrosion resistant charge air cooler and method of making same
US20060195361A1 (en) * 2005-10-01 2006-08-31 Outland Research Location-based demographic profiling system and method of use
US20080032719A1 (en) * 2005-10-01 2008-02-07 Outland Research, Llc Centralized establishment-based tracking and messaging service
US7921109B2 (en) * 2005-10-05 2011-04-05 Yahoo! Inc. Customizable ordering of search results and predictive query generation
US20070083323A1 (en) * 2005-10-07 2007-04-12 Outland Research Personal cuing for spatially associated information
US20060179056A1 (en) * 2005-10-12 2006-08-10 Outland Research Enhanced storage and retrieval of spatially associated information
EP1783631A1 (en) * 2005-11-08 2007-05-09 Lycos Europe GmbH Search result ranking by means of relevance feedback
US8095565B2 (en) * 2005-12-05 2012-01-10 Microsoft Corporation Metadata driven user interface
US7577522B2 (en) * 2005-12-05 2009-08-18 Outland Research, Llc Spatially associated personal reminder system and method
WO2007068881A3 (en) * 2005-12-13 2008-01-17 British Telecomm User specific database querying method and apparatus
US20070075127A1 (en) * 2005-12-21 2007-04-05 Outland Research, Llc Orientation-based power conservation for portable media devices
EP1801720A1 (en) * 2005-12-22 2007-06-27 Microsoft Corporation Authorisation and authentication
US20070150473A1 (en) * 2005-12-22 2007-06-28 Microsoft Corporation Search By Document Type And Relevance
US7599918B2 (en) * 2005-12-29 2009-10-06 Microsoft Corporation Dynamic search with implicit user intention mining
US7925649B2 (en) * 2005-12-30 2011-04-12 Google Inc. Method, system, and graphical user interface for alerting a computer user to new results for a prior search
EP1808786A1 (en) * 2006-01-12 2007-07-18 Yoogli, Inc. User context based search engine
GB0600678D0 (en) * 2006-01-13 2006-02-22 Vodafone Plc Search platform
US7624101B2 (en) * 2006-01-31 2009-11-24 Google Inc. Enhanced search results
WO2007098206A3 (en) * 2006-02-16 2007-12-13 Hillcrest Lab Inc Systems and methods for placing advertisements
US20070198504A1 (en) * 2006-02-23 2007-08-23 Microsoft Corporation Calculating level-based importance of a web page
EP1826695A1 (en) * 2006-02-28 2007-08-29 Microsoft Corporation Secure content descriptions
US20070208730A1 (en) 2006-03-02 2007-09-06 Microsoft Corporation Mining web search user behavior to enhance web search relevance
US8019777B2 (en) * 2006-03-16 2011-09-13 Nexify, Inc. Digital content personalization method and system
US8078607B2 (en) * 2006-03-30 2011-12-13 Google Inc. Generating website profiles based on queries from webistes and user activities on the search results
JP3896383B1 (en) * 2006-04-05 2007-03-22 株式会社アイ・ビジネスセンター Search server, search method, and a program for causing a computer to function as a search server
US20070260597A1 (en) * 2006-05-02 2007-11-08 Mark Cramer Dynamic search engine results employing user behavior
US8442973B2 (en) * 2006-05-02 2013-05-14 Surf Canyon, Inc. Real time implicit user modeling for personalized search
US20150032717A1 (en) * 2006-05-02 2015-01-29 Surf Canyon Incorporated Real time implicit user modeling for personalized search
EP1860575A1 (en) 2006-05-16 2007-11-28 Hurra Communications GmbH Method for evaluating information to be represented on a network page
US7966324B2 (en) * 2006-05-30 2011-06-21 Microsoft Corporation Personalizing a search results page based on search history
EP1862916A1 (en) 2006-06-01 2007-12-05 Microsoft Corporation Indexing Documents for Information Retrieval based on additional feedback fields
US9443022B2 (en) * 2006-06-05 2016-09-13 Google Inc. Method, system, and graphical user interface for providing personalized recommendations of popular search queries
US7761464B2 (en) * 2006-06-19 2010-07-20 Microsoft Corporation Diversifying search results for improved search and personalization
US7624104B2 (en) * 2006-06-22 2009-11-24 Yahoo! Inc. User-sensitive pagerank
US20070299785A1 (en) * 2006-06-23 2007-12-27 Dylan Tullberg Method of searching and classifying funds
US7685192B1 (en) * 2006-06-30 2010-03-23 Amazon Technologies, Inc. Method and system for displaying interest space user communities
US7685199B2 (en) * 2006-07-31 2010-03-23 Microsoft Corporation Presenting information related to topics extracted from event classes
US7577718B2 (en) * 2006-07-31 2009-08-18 Microsoft Corporation Adaptive dissemination of personalized and contextually relevant information
US7849079B2 (en) * 2006-07-31 2010-12-07 Microsoft Corporation Temporal ranking of search results
US20080040674A1 (en) * 2006-08-09 2008-02-14 Puneet K Gupta Folksonomy-Enhanced Enterprise-Centric Collaboration and Knowledge Management System
US8707160B2 (en) * 2006-08-10 2014-04-22 Yahoo! Inc. System and method for inferring user interest based on analysis of user-generated metadata
JP2010503081A (en) * 2006-08-31 2010-01-28 クゥアルコム・インコーポレイテッドQualcomm Incorporated Using the user-based bias results for obtaining or providing a method and apparatus
US7783636B2 (en) * 2006-09-28 2010-08-24 Microsoft Corporation Personalized information retrieval search with backoff
US20080195456A1 (en) * 2006-09-28 2008-08-14 Dudley Fitzpatrick Apparatuses, Methods and Systems for Coordinating Personnel Based on Profiles
EP1909197A1 (en) * 2006-10-03 2008-04-09 Pointer S.R.L. Systems and methods for ranking search engine results
US7680786B2 (en) * 2006-10-30 2010-03-16 Yahoo! Inc. Optimization of targeted advertisements based on user profile information
US20080288588A1 (en) * 2006-11-01 2008-11-20 Worldvuer, Inc. Method and system for searching using image based tagging
US20080176194A1 (en) 2006-11-08 2008-07-24 Nina Zolt System for developing literacy skills using loosely coupled tools in a self-directed learning process within a collaborative social network
JP5393471B2 (en) * 2006-11-08 2014-01-22 イーパルズ インコーポレイテッド Dynamic characteristics of nodes in a semantic network
JP2008146207A (en) * 2006-12-07 2008-06-26 Yuichiro Matsuda Content retrieval method, content retrieval program and recording medium
US9390173B2 (en) * 2006-12-20 2016-07-12 Victor David Uy Method and apparatus for scoring electronic documents
US7987185B1 (en) * 2006-12-29 2011-07-26 Google Inc. Ranking custom search results
US20080168045A1 (en) * 2007-01-10 2008-07-10 Microsoft Corporation Content rank
US7966321B2 (en) 2007-01-17 2011-06-21 Google Inc. Presentation of local results
US7966309B2 (en) * 2007-01-17 2011-06-21 Google Inc. Providing relevance-ordered categories of information
US8005822B2 (en) 2007-01-17 2011-08-23 Google Inc. Location in search queries
US20080183691A1 (en) * 2007-01-30 2008-07-31 International Business Machines Corporation Method for a networked knowledge based document retrieval and ranking utilizing extracted document metadata and content
US8620915B1 (en) 2007-03-13 2013-12-31 Google Inc. Systems and methods for promoting personalized search results based on personal information
US8166021B1 (en) 2007-03-30 2012-04-24 Google Inc. Query phrasification
US8583592B2 (en) * 2007-03-30 2013-11-12 Innography, Inc. System and methods of searching data sources
US7693813B1 (en) 2007-03-30 2010-04-06 Google Inc. Index server architecture using tiered and sharded phrase posting lists
US20080243784A1 (en) * 2007-03-30 2008-10-02 Tyron Jerrod Stading System and methods of query refinement
US8166045B1 (en) 2007-03-30 2012-04-24 Google Inc. Phrase extraction using subphrase scoring
US7925655B1 (en) 2007-03-30 2011-04-12 Google Inc. Query scheduling using hierarchical tiers of index servers
US8285656B1 (en) 2007-03-30 2012-10-09 Consumerinfo.Com, Inc. Systems and methods for data verification
US8086594B1 (en) 2007-03-30 2011-12-27 Google Inc. Bifurcated document relevance scoring
US7702614B1 (en) 2007-03-30 2010-04-20 Google Inc. Index updating using segment swapping
US20080249798A1 (en) * 2007-04-04 2008-10-09 Atul Tulshibagwale Method and System of Ranking Web Content
US7752201B2 (en) * 2007-05-10 2010-07-06 Microsoft Corporation Recommendation of related electronic assets based on user search behavior
US8037042B2 (en) * 2007-05-10 2011-10-11 Microsoft Corporation Automated analysis of user search behavior
US7984068B2 (en) * 2007-05-25 2011-07-19 Google Inc. Providing profile information to partner content providers
EP2838064A1 (en) * 2007-05-25 2015-02-18 Piksel, Inc. Recomendation systems and methods
US7734641B2 (en) 2007-05-25 2010-06-08 Peerset, Inc. Recommendation systems and methods using interest correlation
US20080301582A1 (en) * 2007-05-29 2008-12-04 Tasteindex.Com Llc Taste network widget system
US20080300958A1 (en) * 2007-05-29 2008-12-04 Tasteindex.Com Llc Taste network content targeting
US20080301551A1 (en) * 2007-05-29 2008-12-04 Tasteindex.Com Llc Taste network system and method
US8996409B2 (en) 2007-06-06 2015-03-31 Sony Computer Entertainment Inc. Management of online trading services using mediated communications
US8244737B2 (en) 2007-06-18 2012-08-14 Microsoft Corporation Ranking documents based on a series of document graphs
US20080315331A1 (en) * 2007-06-25 2008-12-25 Robert Gideon Wodnicki Ultrasound system with through via interconnect structure
US9398113B2 (en) 2007-07-07 2016-07-19 Qualcomm Incorporated Methods and systems for providing targeted information using identity masking in a wireless communications device
US9392074B2 (en) 2007-07-07 2016-07-12 Qualcomm Incorporated User profile generation architecture for mobile content-message targeting
US7920849B2 (en) * 2007-07-13 2011-04-05 Pop Adrian Method and system for providing advertisements/messages based on wireless data communication technology
US8027964B2 (en) * 2007-07-13 2011-09-27 Medio Systems, Inc. Personalized query completion suggestion
US8359319B2 (en) * 2007-08-27 2013-01-22 Sudhir Pendse Tool for personalized search
KR101395518B1 (en) * 2007-09-03 2014-05-14 엘지전자 주식회사 Information search system
WO2009030972A1 (en) * 2007-09-06 2009-03-12 Chin San Sathya Wong Method and system of generating and presenting search results
US8117223B2 (en) 2007-09-07 2012-02-14 Google Inc. Integrating external related phrase information into a phrase-based indexing information retrieval system
US7840569B2 (en) * 2007-10-18 2010-11-23 Microsoft Corporation Enterprise relevancy ranking using a neural network
US9348912B2 (en) * 2007-10-18 2016-05-24 Microsoft Technology Licensing, Llc Document length as a static relevance feature for ranking search results
US20090106221A1 (en) * 2007-10-18 2009-04-23 Microsoft Corporation Ranking and Providing Search Results Based In Part On A Number Of Click-Through Features
US9203911B2 (en) 2007-11-14 2015-12-01 Qualcomm Incorporated Method and system for using a cache miss state match indicator to determine user suitability of targeted content messages in a mobile environment
US20090132645A1 (en) * 2007-11-16 2009-05-21 Iac Search & Media, Inc. User interface and method in a local search system with multiple-field comparison
US20090132514A1 (en) * 2007-11-16 2009-05-21 Iac Search & Media, Inc. method and system for building text descriptions in a search database
US20090132573A1 (en) * 2007-11-16 2009-05-21 Iac Search & Media, Inc. User interface and method in a local search system with search results restricted by drawn figure elements
US20090132643A1 (en) * 2007-11-16 2009-05-21 Iac Search & Media, Inc. Persistent local search interface and method
US8145703B2 (en) * 2007-11-16 2012-03-27 Iac Search & Media, Inc. User interface and method in a local search system with related search results
US20090132513A1 (en) * 2007-11-16 2009-05-21 Iac Search & Media, Inc. Correlation of data in a system and method for conducting a search
US8732155B2 (en) * 2007-11-16 2014-05-20 Iac Search & Media, Inc. Categorization in a system and method for conducting a search
US20090132484A1 (en) * 2007-11-16 2009-05-21 Iac Search & Media, Inc. User interface and method in a local search system having vertical context
KR101060487B1 (en) * 2007-11-19 2011-08-30 서울대학교산학협력단 Content recommendation apparatus and method using a tag cloud
US20090138329A1 (en) * 2007-11-26 2009-05-28 William Paul Wanker Application of query weights input to an electronic commerce information system to target advertising
US7930298B2 (en) * 2007-11-27 2011-04-19 Institute For Information Industry System and method for generating 'snapshot's of learning objects
US8291492B2 (en) * 2007-12-12 2012-10-16 Google Inc. Authentication of a contributor of online content
US8127986B1 (en) 2007-12-14 2012-03-06 Consumerinfo.Com, Inc. Card registry systems and methods
US9391789B2 (en) 2007-12-14 2016-07-12 Qualcomm Incorporated Method and system for multi-level distribution information cache management in a mobile environment
US20090198488A1 (en) * 2008-02-05 2009-08-06 Eric Arno Vigen System and method for analyzing communications using multi-placement hierarchical structures
US8244721B2 (en) * 2008-02-13 2012-08-14 Microsoft Corporation Using related users data to enhance web search
US20090210391A1 (en) * 2008-02-14 2009-08-20 Hall Stephen G Method and system for automated search for, and retrieval and distribution of, information
US8412702B2 (en) * 2008-03-12 2013-04-02 Yahoo! Inc. System, method, and/or apparatus for reordering search results
US20090234876A1 (en) * 2008-03-14 2009-09-17 Timothy Schigel Systems and methods for content sharing
US8762364B2 (en) * 2008-03-18 2014-06-24 Yahoo! Inc. Personalizing sponsored search advertising layout using user behavior history
US8359312B2 (en) * 2008-03-26 2013-01-22 Amiram Grynberg Methods for generating a personalized list of documents associated with a search query
EP2105846A1 (en) * 2008-03-28 2009-09-30 Sony Corporation Method of recommending content items
US8812493B2 (en) * 2008-04-11 2014-08-19 Microsoft Corporation Search results ranking using editing distance and document information
US9135328B2 (en) * 2008-04-30 2015-09-15 Yahoo! Inc. Ranking documents through contextual shortcuts
EP2120179A1 (en) 2008-05-16 2009-11-18 Swisscom AG Method for modelling a user
US8510262B2 (en) * 2008-05-21 2013-08-13 Microsoft Corporation Promoting websites based on location
US8117197B1 (en) 2008-06-10 2012-02-14 Surf Canyon, Inc. Adaptive user interface for real-time search relevance feedback
US9268843B2 (en) * 2008-06-27 2016-02-23 Cbs Interactive Inc. Personalization engine for building a user profile
US20100235231A1 (en) * 2009-01-30 2010-09-16 Cbs Interactive, Inc. Lead acquisition, promotion and inventory management system and method
US8214346B2 (en) * 2008-06-27 2012-07-03 Cbs Interactive Inc. Personalization engine for classifying unstructured documents
US20090327270A1 (en) * 2008-06-27 2009-12-31 Microsoft Corporation Using Variation in User Interest to Enhance the Search Experience
US8346749B2 (en) * 2008-06-27 2013-01-01 Microsoft Corporation Balancing the costs of sharing private data with the utility of enhanced personalization of online services
JP5196150B2 (en) * 2008-06-30 2013-05-15 株式会社エクォス・リサーチ The information processing apparatus, information processing method, and program
US8180771B2 (en) * 2008-07-18 2012-05-15 Iac Search & Media, Inc. Search activity eraser
JP4981765B2 (en) * 2008-08-05 2012-07-25 ヤフー株式会社 Search processing system to personalize search processing in Web search using the click history, the terminal apparatus and a search processing method
US9367618B2 (en) * 2008-08-07 2016-06-14 Yahoo! Inc. Context based search arrangement for mobile devices
US9256904B1 (en) 2008-08-14 2016-02-09 Experian Information Solutions, Inc. Multi-bureau credit file freeze and unfreeze
CN101661475B (en) * 2008-08-26 2013-04-24 华为技术有限公司 Search method and system
US20100082684A1 (en) * 2008-10-01 2010-04-01 Yahoo! Inc. Method and system for providing personalized web experience
US9460212B2 (en) * 2008-12-03 2016-10-04 Paypal, Inc. System and method for personalized search
US8595228B1 (en) * 2009-01-09 2013-11-26 Google Inc. Preferred sites
US9195640B1 (en) 2009-01-12 2015-11-24 Sri International Method and system for finding content having a desired similarity
US9600581B2 (en) * 2009-02-19 2017-03-21 Yahoo! Inc. Personalized recommendations on dynamic content
US20100287129A1 (en) * 2009-05-07 2010-11-11 Yahoo!, Inc., a Delaware corporation System, method, or apparatus relating to categorizing or selecting potential search results
US8489515B2 (en) * 2009-05-08 2013-07-16 Comcast Interactive Media, LLC. Social network based recommendation method and system
WO2010132492A3 (en) 2009-05-11 2014-03-20 Experian Marketing Solutions, Inc. Systems and methods for providing anonymized user profile data
US20100299140A1 (en) * 2009-05-22 2010-11-25 Cycorp, Inc. Identifying and routing of documents of potential interest to subscribers using interest determination rules
US9602444B2 (en) * 2009-05-28 2017-03-21 Google Inc. Participant suggestion system
US20100318533A1 (en) * 2009-06-10 2010-12-16 Yahoo! Inc. Enriched document representations using aggregated anchor text
US8924846B2 (en) * 2009-07-03 2014-12-30 Hewlett-Packard Development Company, L.P. Apparatus and method for text extraction
US8521680B2 (en) * 2009-07-31 2013-08-27 Microsoft Corporation Inferring user-specific location semantics from user data
US20110035375A1 (en) * 2009-08-06 2011-02-10 Ron Bekkerman Building user profiles for website personalization
US20110040753A1 (en) * 2009-08-11 2011-02-17 Steve Knight Personalized search engine
US20110136542A1 (en) * 2009-12-09 2011-06-09 Nokia Corporation Method and apparatus for suggesting information resources based on context and preferences
US20110179025A1 (en) * 2010-01-21 2011-07-21 Kryptonite Systems Inc Social and contextual searching for enterprise business applications
US20110208732A1 (en) 2010-02-24 2011-08-25 Apple Inc. Systems and methods for organizing data items
US20110225139A1 (en) * 2010-03-11 2011-09-15 Microsoft Corporation User role based customizable semantic search
US8244766B2 (en) 2010-04-13 2012-08-14 Microsoft Corporation Applying a model of a persona to search results
US9785987B2 (en) 2010-04-22 2017-10-10 Microsoft Technology Licensing, Llc User interface for information presentation system
CN102253943B (en) * 2010-05-21 2013-09-11 卓望数码技术(深圳)有限公司 Webpage rating method and webpage rating system
US20110295612A1 (en) * 2010-05-28 2011-12-01 Thierry Donneau-Golencer Method and apparatus for user modelization
US8738635B2 (en) 2010-06-01 2014-05-27 Microsoft Corporation Detection of junk in search result ranking
US9552422B2 (en) 2010-06-11 2017-01-24 Doat Media Ltd. System and method for detecting a search intent
US9529918B2 (en) 2010-06-11 2016-12-27 Doat Media Ltd. System and methods thereof for downloading applications via a communication network
US8326861B1 (en) * 2010-06-23 2012-12-04 Google Inc. Personalized term importance evaluation in queries
US8316021B2 (en) * 2010-06-30 2012-11-20 Emergency 24, Inc. Methods and systems for enhanced placement search engine based on user usage
US8504487B2 (en) 2010-09-21 2013-08-06 Sony Computer Entertainment America Llc Evolution of a user interface based on learned idiosyncrasies and collected data of a user
US8484219B2 (en) * 2010-09-21 2013-07-09 Sony Computer Entertainment America Llc Developing a knowledge base associated with a user that facilitates evolution of an intelligent user interface
US8875007B2 (en) * 2010-11-08 2014-10-28 Microsoft Corporation Creating and modifying an image wiki page
US8484186B1 (en) 2010-11-12 2013-07-09 Consumerinfo.Com, Inc. Personalized people finder
US9147042B1 (en) 2010-11-22 2015-09-29 Experian Information Solutions, Inc. Systems and methods for data verification
US8793706B2 (en) 2010-12-16 2014-07-29 Microsoft Corporation Metadata-based eventing supporting operations on data
CN102591876A (en) * 2011-01-14 2012-07-18 阿里巴巴集团控股有限公司 Sequencing method and device of search results
US8484098B2 (en) 2011-03-03 2013-07-09 Michael Bilotta System for information delivery facilitating partner rating of users and user ratings of partners
US8983995B2 (en) 2011-04-15 2015-03-17 Microsoft Corporation Interactive semantic query suggestion for content search
US9558519B1 (en) 2011-04-29 2017-01-31 Consumerinfo.Com, Inc. Exposing reporting cycle information
US8438165B2 (en) 2011-05-12 2013-05-07 Microsoft Corporation Interest tracking using shared search queries and interactions
US8983924B2 (en) 2011-05-12 2015-03-17 Microsoft Technology Licensing, Llc Sharing public search queries and interactions
US9384504B2 (en) 2012-06-13 2016-07-05 Aggregate Shopping Corp. System and method for a user to perform online searching and purchasing of multiple items
US9224167B2 (en) 2012-06-13 2015-12-29 Aggregate Shopping Corp. System and method for aiding user in online searching and purchasing of multiple items
US9607336B1 (en) 2011-06-16 2017-03-28 Consumerinfo.Com, Inc. Providing credit inquiry alerts
JP5800184B2 (en) * 2011-07-14 2015-10-28 日本電気株式会社 The information processing system, the method of action promoting the user, the information processing apparatus and control method thereof control program
US20130024439A1 (en) * 2011-07-20 2013-01-24 Microsoft Corporation Modeling search in a social graph
CN102937951B (en) * 2011-08-15 2016-11-02 北京百度网讯科技有限公司 Ip address of the method for establishing the classification model, method and apparatus for user classification
US9106691B1 (en) 2011-09-16 2015-08-11 Consumerinfo.Com, Inc. Systems and methods of identity protection and management
US8738516B1 (en) 2011-10-13 2014-05-27 Consumerinfo.Com, Inc. Debt services candidate locator
US8868590B1 (en) 2011-11-17 2014-10-21 Sri International Method and system utilizing a personalized user model to develop a search request
US8370348B1 (en) 2011-12-06 2013-02-05 Google Inc. Magazine edition recommendations
US8612851B2 (en) 2011-12-06 2013-12-17 Google Inc. Edition grid layout
US9110998B2 (en) 2011-12-22 2015-08-18 Google Technology Holdings LLC Hierarchical behavioral profile
US20130166609A1 (en) * 2011-12-22 2013-06-27 General Instrument Corporation Hierarchical behavioral profile
US8943015B2 (en) 2011-12-22 2015-01-27 Google Technology Holdings LLC Hierarchical behavioral profile
US8862597B2 (en) * 2011-12-27 2014-10-14 Sap Portals Israel Ltd Providing contextually-relevant content
US9495462B2 (en) 2012-01-27 2016-11-15 Microsoft Technology Licensing, Llc Re-ranking search results
US8521735B1 (en) 2012-02-27 2013-08-27 Google Inc. Anonymous personalized recommendation method
JP5885689B2 (en) * 2012-03-06 2016-03-15 株式会社オウケイウェイヴ Q & a system
US20130246392A1 (en) * 2012-03-14 2013-09-19 Inago Inc. Conversational System and Method of Searching for Information
CN102622445B (en) * 2012-03-15 2014-05-07 华南理工大学 User interest perception based webpage push system and webpage push method
US9092052B2 (en) * 2012-04-10 2015-07-28 Andreas Kornstädt Method and apparatus for obtaining entity-related decision support information based on user-supplied preferences
JP5238895B1 (en) * 2012-04-26 2013-07-17 楽天株式会社 The information processing apparatus, information processing method, information processing program and a recording medium
US9853959B1 (en) 2012-05-07 2017-12-26 Consumerinfo.Com, Inc. Storage and maintenance of personal data
US20130325846A1 (en) * 2012-06-01 2013-12-05 Google Inc. Latent collaborative retrieval
US9053177B1 (en) * 2012-06-11 2015-06-09 Google Inc. Sitelinks based on visual location
US9245428B2 (en) 2012-08-02 2016-01-26 Immersion Corporation Systems and methods for haptic remote control gaming
WO2014032708A1 (en) 2012-08-29 2014-03-06 Iiinnovation S.A. Method of operating a tv receiver and tv receiver
US9189555B2 (en) * 2012-09-07 2015-11-17 Oracle International Corporation Displaying customized list of links to content using client-side processing
US8745074B1 (en) * 2012-09-14 2014-06-03 Google Inc. Method and system for evaluating content via a computer network
US8886644B1 (en) * 2012-11-01 2014-11-11 Google Inc. User control of search filter bubble
US9654541B1 (en) 2012-11-12 2017-05-16 Consumerinfo.Com, Inc. Aggregating user web browsing data
WO2014079534A1 (en) * 2012-11-26 2014-05-30 Alcatel Lucent System and method for determination of personalization in online service provider responses
US9830646B1 (en) 2012-11-30 2017-11-28 Consumerinfo.Com, Inc. Credit score goals and alerts systems and methods
US9105178B2 (en) 2012-12-03 2015-08-11 Sony Computer Entertainment Inc. Remote dynamic configuration of telemetry reporting through regular expressions
US9278255B2 (en) 2012-12-09 2016-03-08 Arris Enterprises, Inc. System and method for activity recognition
US9582572B2 (en) * 2012-12-19 2017-02-28 Intel Corporation Personalized search library based on continual concept correlation
US9141657B2 (en) 2012-12-21 2015-09-22 Samsung Electronics Co., Ltd. Content delivery system with profile generation mechanism and method of operation thereof
US9697263B1 (en) 2013-03-04 2017-07-04 Experian Information Solutions, Inc. Consumer data request fulfillment system
US9406085B1 (en) 2013-03-14 2016-08-02 Consumerinfo.Com, Inc. System and methods for credit dispute processing, resolution, and reporting
US20140280083A1 (en) * 2013-03-14 2014-09-18 Vmware,Inc. Event based object ranking in a dynamic system
US20140279505A1 (en) * 2013-03-14 2014-09-18 Bank Of America Corporation Recommending vehicle for payment based on social network data
US9870589B1 (en) 2013-03-14 2018-01-16 Consumerinfo.Com, Inc. Credit utilization tracking and reporting
US9501506B1 (en) 2013-03-15 2016-11-22 Google Inc. Indexing system
US9311362B1 (en) * 2013-03-15 2016-04-12 Google Inc. Personal knowledge panel interface
WO2014162397A1 (en) * 2013-04-01 2014-10-09 株式会社日立製作所 Computer system, data management method, and computer
US9547698B2 (en) 2013-04-23 2017-01-17 Google Inc. Determining media consumption preferences
US9721147B1 (en) 2013-05-23 2017-08-01 Consumerinfo.Com, Inc. Digital identity
US9483568B1 (en) 2013-06-05 2016-11-01 Google Inc. Indexing system
US20150012558A1 (en) * 2013-07-02 2015-01-08 Google Inc. Using models to annotate search queries
US20150012532A1 (en) * 2013-07-02 2015-01-08 Google Inc. User models for implicit intents in search
US20150012524A1 (en) * 2013-07-02 2015-01-08 Google Inc. Using models for triggering personal search
US9727652B2 (en) * 2013-07-22 2017-08-08 International Business Machines Corporation Utilizing dependency among internet search results
US20150039606A1 (en) * 2013-08-01 2015-02-05 Vamsi Krishna Salaka Search phrase modification
US9760608B2 (en) * 2013-11-01 2017-09-12 Microsoft Technology Licensing, Llc Real-time search tuning
US9477737B1 (en) 2013-11-20 2016-10-25 Consumerinfo.Com, Inc. Systems and user interfaces for dynamic access of multiple remote databases and synchronization of data based on user rules
US9529851B1 (en) 2013-12-02 2016-12-27 Experian Information Solutions, Inc. Server architecture for electronic data quality processing
US9633083B2 (en) * 2013-12-05 2017-04-25 Lenovo (Singapore) Pte. Ltd. Organizing search results using smart tag inferences
US9818065B2 (en) 2014-03-12 2017-11-14 Microsoft Technology Licensing, Llc Attribution of activity in multi-user settings
USD759690S1 (en) 2014-03-25 2016-06-21 Consumerinfo.Com, Inc. Display screen or portion thereof with graphical user interface
USD760256S1 (en) 2014-03-25 2016-06-28 Consumerinfo.Com, Inc. Display screen or portion thereof with graphical user interface
USD759689S1 (en) 2014-03-25 2016-06-21 Consumerinfo.Com, Inc. Display screen or portion thereof with graphical user interface
CN103955537A (en) * 2014-05-16 2014-07-30 福州大学 Method and system for designing searchable encrypted cloud disc with fuzzy semantics
US20160034586A1 (en) * 2014-07-30 2016-02-04 Linkedin Corporation Behavior influenced search ranking
CN104217030A (en) * 2014-09-28 2014-12-17 北京奇虎科技有限公司 Method and device for classifying users according to search log data of server
US9794746B2 (en) * 2014-12-05 2017-10-17 Apple Inc. Dynamic content presentation based on proximity and user data
CN104504251B (en) * 2014-12-10 2017-12-15 沈阳航空航天大学 Community division method based on PageRank algorithm
US20160188659A1 (en) * 2014-12-31 2016-06-30 Yuanjie Liu Determining search results using session based refinements
US9838387B2 (en) * 2015-04-28 2017-12-05 Management Systems Resources Inc. Security token with embedded data
US20160371270A1 (en) * 2015-06-16 2016-12-22 Salesforce.Com, Inc. Processing a file to generate a recommendation using a database system
CN104933172A (en) * 2015-06-30 2015-09-23 百度在线网络技术(北京)有限公司 Information pushing method and device based on user searching behavior
US20170124081A1 (en) * 2015-11-02 2017-05-04 International Business Machines Corporation Rank-based calculation for keyword searches
US9613221B1 (en) * 2015-12-30 2017-04-04 Quixey, Inc. Signed application cards

Citations (17)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US5742567A (en) * 1995-09-07 1998-04-21 Pioneer Electronic Corporation Master optical disk recording apparatus
US5754939A (en) * 1994-11-29 1998-05-19 Herz; Frederick S. M. System for generation of user profiles for a system for customized electronic identification of desirable objects
US5848396A (en) * 1996-04-26 1998-12-08 Freedom Of Information, Inc. Method and apparatus for determining behavioral profile of a computer user
US6006218A (en) * 1997-02-28 1999-12-21 Microsoft Methods and apparatus for retrieving and/or processing retrieved information as a function of a user's estimated knowledge
US6285999B1 (en) * 1997-01-10 2001-09-04 The Board Of Trustees Of The Leland Stanford Junior University Method for node ranking in a linked database
US6327590B1 (en) * 1999-05-05 2001-12-04 Xerox Corporation System and method for collaborative ranking of search results employing user and group profiles derived from document collection content analysis
US20020024532A1 (en) * 2000-08-25 2002-02-28 Wylci Fables Dynamic personalization method of creating personalized user profiles for searching a database of information
US6385619B1 (en) * 1999-01-08 2002-05-07 International Business Machines Corporation Automatic user interest profile generation from structured document access information
US20020198882A1 (en) * 2001-03-29 2002-12-26 Linden Gregory D. Content personalization based on actions performed during a current browsing session
US20030033333A1 (en) * 2001-05-11 2003-02-13 Fujitsu Limited Hot topic extraction apparatus and method, storage medium therefor
US6584468B1 (en) * 2000-09-29 2003-06-24 Ninesigma, Inc. Method and apparatus to retrieve information from a network
US6647381B1 (en) * 1999-10-27 2003-11-11 Nec Usa, Inc. Method of defining and utilizing logical domains to partition and to reorganize physical domains
US20030233345A1 (en) * 2002-06-14 2003-12-18 Igor Perisic System and method for personalized information retrieval based on user expertise
US20040044571A1 (en) * 2002-08-27 2004-03-04 Bronnimann Eric Robert Method and system for providing advertising listing variance in distribution feeds over the internet to maximize revenue to the advertising distributor
US20040267700A1 (en) * 2003-06-26 2004-12-30 Dumais Susan T. Systems and methods for personal ubiquitous information retrieval and reuse
US6912505B2 (en) * 1998-09-18 2005-06-28 Amazon.Com, Inc. Use of product viewing histories of users to identify related products
US6981040B1 (en) * 1999-12-28 2005-12-27 Utopy, Inc. Automatic, personalized online information and product services

Family Cites Families (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US5724567A (en) * 1994-04-25 1998-03-03 Apple Computer, Inc. System for directing relevance-ranked data objects to computer users
US6353398B1 (en) * 1999-10-22 2002-03-05 Himanshu S. Amin System for dynamically pushing information to a user utilizing global positioning system
WO2001042981A3 (en) * 1999-12-07 2003-12-24 Otman Basir Natural english language search and retrieval system and method
JP2002032401A (en) * 2000-07-18 2002-01-31 Mitsubishi Electric Corp Method and device for document retrieval and computer- readable recording medium with recorded program making computer actualize method for document retrieving
JP2002259720A (en) * 2001-03-02 2002-09-13 Internatl Business Mach Corp <Ibm> Contents summarizing system, image summarizing system, user terminal unit, summary image producing method, summary image receiving method, and program

Patent Citations (17)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US5754939A (en) * 1994-11-29 1998-05-19 Herz; Frederick S. M. System for generation of user profiles for a system for customized electronic identification of desirable objects
US5742567A (en) * 1995-09-07 1998-04-21 Pioneer Electronic Corporation Master optical disk recording apparatus
US5848396A (en) * 1996-04-26 1998-12-08 Freedom Of Information, Inc. Method and apparatus for determining behavioral profile of a computer user
US6285999B1 (en) * 1997-01-10 2001-09-04 The Board Of Trustees Of The Leland Stanford Junior University Method for node ranking in a linked database
US6006218A (en) * 1997-02-28 1999-12-21 Microsoft Methods and apparatus for retrieving and/or processing retrieved information as a function of a user's estimated knowledge
US6912505B2 (en) * 1998-09-18 2005-06-28 Amazon.Com, Inc. Use of product viewing histories of users to identify related products
US6385619B1 (en) * 1999-01-08 2002-05-07 International Business Machines Corporation Automatic user interest profile generation from structured document access information
US6327590B1 (en) * 1999-05-05 2001-12-04 Xerox Corporation System and method for collaborative ranking of search results employing user and group profiles derived from document collection content analysis
US6647381B1 (en) * 1999-10-27 2003-11-11 Nec Usa, Inc. Method of defining and utilizing logical domains to partition and to reorganize physical domains
US6981040B1 (en) * 1999-12-28 2005-12-27 Utopy, Inc. Automatic, personalized online information and product services
US20020024532A1 (en) * 2000-08-25 2002-02-28 Wylci Fables Dynamic personalization method of creating personalized user profiles for searching a database of information
US6584468B1 (en) * 2000-09-29 2003-06-24 Ninesigma, Inc. Method and apparatus to retrieve information from a network
US20020198882A1 (en) * 2001-03-29 2002-12-26 Linden Gregory D. Content personalization based on actions performed during a current browsing session
US20030033333A1 (en) * 2001-05-11 2003-02-13 Fujitsu Limited Hot topic extraction apparatus and method, storage medium therefor
US20030233345A1 (en) * 2002-06-14 2003-12-18 Igor Perisic System and method for personalized information retrieval based on user expertise
US20040044571A1 (en) * 2002-08-27 2004-03-04 Bronnimann Eric Robert Method and system for providing advertising listing variance in distribution feeds over the internet to maximize revenue to the advertising distributor
US20040267700A1 (en) * 2003-06-26 2004-12-30 Dumais Susan T. Systems and methods for personal ubiquitous information retrieval and reuse

Cited By (71)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20080109422A1 (en) * 2006-11-02 2008-05-08 Yahoo! Inc. Personalized search
US9519715B2 (en) * 2006-11-02 2016-12-13 Excalibur Ip, Llc Personalized search
US20090030923A1 (en) * 2007-07-26 2009-01-29 International Business Machines Corporation Identification of shared resources
US8538940B2 (en) * 2007-07-26 2013-09-17 International Business Machines Corporation Identification of shared resources
US20090119248A1 (en) * 2007-11-02 2009-05-07 Neelakantan Sundaresan Search based on diversity
US20160012109A1 (en) * 2007-11-02 2016-01-14 Ebay Inc. Search based on diversity
US9619515B2 (en) * 2007-11-02 2017-04-11 Ebay Inc. Search based on diversity
US9152699B2 (en) * 2007-11-02 2015-10-06 Ebay Inc. Search based on diversity
US20090216749A1 (en) * 2007-11-28 2009-08-27 Blame Canada Holdings Inc. Identity based content filtering
US20110035388A1 (en) * 2008-01-02 2011-02-10 Samsung Electronics Co., Ltd. Method and apparatus for recommending information using a hybrid algorithm
US20100023952A1 (en) * 2008-02-25 2010-01-28 Michael Sandoval Platform for data aggregation, communication, rule evaluation, and combinations thereof, using templated auto-generation
US8402081B2 (en) 2008-02-25 2013-03-19 Atigeo, LLC Platform for data aggregation, communication, rule evaluation, and combinations thereof, using templated auto-generation
US20090216639A1 (en) * 2008-02-25 2009-08-27 Mark Joseph Kapczynski Advertising selection and display based on electronic profile information
US20090216563A1 (en) * 2008-02-25 2009-08-27 Michael Sandoval Electronic profile development, storage, use and systems for taking action based thereon
US20090216750A1 (en) * 2008-02-25 2009-08-27 Michael Sandoval Electronic profile development, storage, use, and systems therefor
US8255396B2 (en) * 2008-02-25 2012-08-28 Atigeo Llc Electronic profile development, storage, use, and systems therefor
US20160350797A1 (en) * 2008-03-31 2016-12-01 Yahoo! Inc. Ranking advertisements with pseudo-relevance feedback and translation models
US20090292688A1 (en) * 2008-05-23 2009-11-26 Yahoo! Inc. Ordering relevant content by time for determining top picks
US8782557B2 (en) * 2008-06-26 2014-07-15 Microsoft Corporation Ordered multiple selection user interface
US20090327960A1 (en) * 2008-06-26 2009-12-31 Microsoft Corporation Ordered Multiple Selection User Interface
US20110029515A1 (en) * 2009-07-31 2011-02-03 Scholz Martin B Method and system for providing website content
US8984647B2 (en) 2010-05-06 2015-03-17 Atigeo Llc Systems, methods, and computer readable media for security in profile utilizing systems
US9164801B2 (en) 2010-06-08 2015-10-20 International Business Machines Corporation Probabilistic optimization of resource discovery, reservation and assignment
US8560365B2 (en) 2010-06-08 2013-10-15 International Business Machines Corporation Probabilistic optimization of resource discovery, reservation and assignment
US9665647B2 (en) 2010-06-11 2017-05-30 Doat Media Ltd. System and method for indexing mobile applications
US9846699B2 (en) 2010-06-11 2017-12-19 Doat Media Ltd. System and methods thereof for dynamically updating the contents of a folder on a device
US9639611B2 (en) * 2010-06-11 2017-05-02 Doat Media Ltd. System and method for providing suitable web addresses to a user device
US9646271B2 (en) 2010-08-06 2017-05-09 International Business Machines Corporation Generating candidate inclusion/exclusion cohorts for a multiply constrained group
US8370350B2 (en) 2010-09-03 2013-02-05 International Business Machines Corporation User accessibility to resources enabled through adaptive technology
US8968197B2 (en) 2010-09-03 2015-03-03 International Business Machines Corporation Directing a user to a medical resource
US20120072460A1 (en) * 2010-09-17 2012-03-22 International Business Machines Corporation User accessibility to data analytics
US9292577B2 (en) * 2010-09-17 2016-03-22 International Business Machines Corporation User accessibility to data analytics
US9443211B2 (en) 2010-10-13 2016-09-13 International Business Machines Corporation Describing a paradigmatic member of a task directed community in a complex heterogeneous environment based on non-linear attributes
US8429182B2 (en) 2010-10-13 2013-04-23 International Business Machines Corporation Populating a task directed community in a complex heterogeneous environment based on non-linear attributes of a paradigmatic cohort member
US8738613B2 (en) 2011-01-13 2014-05-27 International Business Machines Corporation Relevancy ranking of search results in a network based upon a user's computer-related activities
US8688691B2 (en) * 2011-01-13 2014-04-01 International Business Machines Corporation Relevancy ranking of search results in a network based upon a user's computer-related activities
US20120185472A1 (en) * 2011-01-13 2012-07-19 International Business Machines Corporation Relevancy Ranking of Search Results in a Network Based Upon a User's Computer-Related Activities
US8095534B1 (en) 2011-03-14 2012-01-10 Vizibility Inc. Selection and sharing of verified search results
US9858342B2 (en) 2011-03-28 2018-01-02 Doat Media Ltd. Method and system for searching for applications respective of a connectivity mode of a user device
US9529915B2 (en) * 2011-06-16 2016-12-27 Microsoft Technology Licensing, Llc Search results based on user and result profiles
US20120323876A1 (en) * 2011-06-16 2012-12-20 Microsoft Corporation Search results based on user and result profiles
WO2012173903A3 (en) * 2011-06-16 2013-04-18 Microsoft Corporation Search results based on user and result profiles
WO2012173903A2 (en) * 2011-06-16 2012-12-20 Microsoft Corporation Search results based on user and result profiles
US8700544B2 (en) 2011-06-17 2014-04-15 Microsoft Corporation Functionality for personalizing search results
US9679064B2 (en) * 2011-06-30 2017-06-13 Nokia Technologies Oy Method and apparatus for providing user-corrected search results
US20140095496A1 (en) * 2011-06-30 2014-04-03 Nokia Corporation Method and apparatus for providing user-corrected search results
US9600585B2 (en) 2011-09-06 2017-03-21 Microsoft Technology Licensing, Llc Using reading levels in responding to requests
US8954423B2 (en) 2011-09-06 2015-02-10 Microsoft Technology Licensing, Llc Using reading levels in responding to requests
US9253282B2 (en) 2011-10-18 2016-02-02 Qualcomm Incorporated Method and apparatus for generating, using, or updating an enriched user profile
US20130297582A1 (en) * 2012-04-09 2013-11-07 Eli Zukovsky Peer sharing of personalized views of detected information based on relevancy to a particular user's personal interests
US20130297590A1 (en) * 2012-04-09 2013-11-07 Eli Zukovsky Detecting and presenting information to a user based on relevancy to the user's personal interest
US20130290339A1 (en) * 2012-04-27 2013-10-31 Yahoo! Inc. User modeling for personalized generalized content recommendations
US8996530B2 (en) * 2012-04-27 2015-03-31 Yahoo! Inc. User modeling for personalized generalized content recommendations
US9785883B2 (en) 2012-04-27 2017-10-10 Excalibur Ip, Llc Avatars for use with personalized generalized content recommendations
US9836545B2 (en) 2012-04-27 2017-12-05 Yahoo Holdings, Inc. Systems and methods for personalized generalized content recommendations
US9607049B2 (en) 2012-07-25 2017-03-28 Ebay Inc. Systems and methods to build and utilize a search infrastructure
US9158768B2 (en) 2012-07-25 2015-10-13 Paypal, Inc. System and methods to configure a query language using an operator dictionary
US20140032517A1 (en) * 2012-07-25 2014-01-30 Ebay Inc. System and methods to configure a profile to rank search results
US9460151B2 (en) 2012-07-25 2016-10-04 Paypal, Inc. System and methods to configure a query language using an operator dictionary
US9275149B2 (en) 2012-08-22 2016-03-01 International Business Machines Corporation Utilizing social network relevancy as a factor in ranking search results
US9053345B2 (en) 2012-09-18 2015-06-09 Samsung Electronics Co., Ltd. Computing system with privacy mechanism and method of operation thereof
US8930353B2 (en) * 2013-01-04 2015-01-06 International Business Machines Corporation System and method for reflective searching of previous search results
US8930355B2 (en) * 2013-01-04 2015-01-06 International Business Machines Corporation System and method for reflective searching of previous search results
US20140195526A1 (en) * 2013-01-04 2014-07-10 International Business Machines Corporation System and method for reflective searching of previous search results
US20140195528A1 (en) * 2013-01-04 2014-07-10 International Business Machines Corporation System and method for reflective searching of previous search results
US20140195244A1 (en) * 2013-01-07 2014-07-10 Samsung Electronics Co., Ltd. Display apparatus and method of controlling display apparatus
WO2014185742A1 (en) * 2013-05-16 2014-11-20 Samsung Electronics Co., Ltd. Computing system with privacy mechanism and method of operation thereof
US9727545B1 (en) * 2013-12-04 2017-08-08 Google Inc. Selecting textual representations for entity attribute values
US20150161092A1 (en) * 2013-12-05 2015-06-11 Lenovo (Singapore) Pte. Ltd. Prioritizing smart tag creation
US20150293913A1 (en) * 2014-04-10 2015-10-15 Ca, Inc. Content augmentation based on a content collection's membership
US9886674B2 (en) 2016-07-25 2018-02-06 International Business Machines Corporation Describing a paradigmatic member of a task directed community in a complex heterogeneous environment based on non-linear attributes

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