EP1834249A2 - Procede, systeme et interface utilisateur graphique permettant de fournir des avis concernant un produit - Google Patents

Procede, systeme et interface utilisateur graphique permettant de fournir des avis concernant un produit

Info

Publication number
EP1834249A2
EP1834249A2 EP05848898A EP05848898A EP1834249A2 EP 1834249 A2 EP1834249 A2 EP 1834249A2 EP 05848898 A EP05848898 A EP 05848898A EP 05848898 A EP05848898 A EP 05848898A EP 1834249 A2 EP1834249 A2 EP 1834249A2
Authority
EP
European Patent Office
Prior art keywords
product
reviews
review
information
search
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Withdrawn
Application number
EP05848898A
Other languages
German (de)
English (en)
Other versions
EP1834249A4 (fr
Inventor
Jan M. Ruhl
Mayur D. Datar
Jessica Y. Lee
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Google LLC
Original Assignee
Google LLC
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Priority claimed from US11/012,846 external-priority patent/US20060143158A1/en
Priority claimed from US11/012,500 external-priority patent/US7962461B2/en
Application filed by Google LLC filed Critical Google LLC
Publication of EP1834249A2 publication Critical patent/EP1834249A2/fr
Publication of EP1834249A4 publication Critical patent/EP1834249A4/fr
Withdrawn legal-status Critical Current

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Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00—Commerce
    • G06Q30/02—Marketing; Price estimation or determination; Fundraising

Definitions

  • the disclosed embodiments relate generally to search engines. More particularly, the disclosed embodiments relate to methods, systems, and graphical user interfaces for finding, aggregating, and providing reviews for a product.
  • users may go to Web sites that provide reviews and ratings for products, such as www.amazon.com, www.epinions.com, and www.circuitcity.com.
  • a few sites such as www.rottentomatoes.com, movies.yahoo.com, and www.consumersearch.com, aggregate reviews for the same product from several sites.
  • some of these sites aggregate reviews manually, which is very inefficient and limits the number of reviews that can be gathered for each product.
  • all of these sites are very limited in the tools they provide users to navigate through the reviews.
  • a server collects information containing product reviews for a plurality of products and automatically extracts the product reviews from the collected information. For at least some of the extracted product reviews, the server identifies a particular product that is associated with the extracted product review. For each particular product in at least a subset of the plurality of products, the server generates aggregated review information for the particular product based on a plurality of extracted product reviews that are associated with the particular product. The server stores the extracted product reviews and the aggregated review information.
  • a server collects product reviews for a plurality of products and automatically identifies particular products that are associated with particular product reviews. For each particular product in at least a subset of the plurality of products, the server automatically generates aggregated review information for the particular product including frequently appearing phrases in the product reviews associated with the particular product. The server automatically stores the product reviews and the aggregated review information.
  • a client sends a request for an aggregated review for a product, receives the aggregated review, and displays the aggregated review.
  • the aggregated review includes portions of reviews for the product and a search input area for entering search terms to search for reviews of the product that contain the search terms.
  • a server receives a search request from a client computer to search within a plurality of reviews for a particular product, obtains information satisfying the request, and sends at least a portion of the obtained information to the client computer.
  • the sent information includes at least portions of reviews for the particular product that satisfy the search request.
  • Another aspect of the invention is a graphical user interface on a computer that includes a plurality of portions of reviews for a product and a search input area for entering search terms to search for reviews of the product that contain the search terms.
  • product refers to both products and services. Moreover, “product” encompasses virtually any product, service or combination thereof that can be bought, leased, rented, or similarly obtained. Exemplary products include, without limitation, consumer products, business products, movies, books, restaurants, hotels, and travel packages. BRIEF DESCRIPTION OF THE DRAWINGS
  • Figure 1 is a block diagram illustrating an exemplary distributed computer system according to an embodiment of the invention.
  • Figure 2A is a block diagram illustrating an aggregated reviews backend server 102 in accordance with one embodiment of the present invention.
  • Figure 2B is a block diagram illustrating an aggregated reviews frontend server 100 in accordance with one embodiment of the present invention.
  • Figure 3 is a flowchart representing a method for collecting and aggregating reviews for a product according to one embodiment.
  • Figure 4 is a flowchart representing a method for displaying an aggregated review for a product according to one embodiment.
  • Figure 5 is a flowchart representing a method for searching aggregated reviews for a product according to one embodiment.
  • Figures 6A-6D are schematic screen shots of an exemplary graphical user interface of a product information module ( Figure 6A) and a product review presentation module ( Figures 6B-6D) according to one embodiment.
  • FIG. 1 is a block diagram illustrating an exemplary distributed computer system according to an embodiment of the invention.
  • This system includes client computer 104, aggregated reviews backend server 102, aggregated reviews frontend server 100, multiple Web sites such as Web sites 108 and 110, and communication network(s) 106 for interconnecting these components.
  • Client 104 includes graphical user interface (GUI) 112.
  • GUI graphical user interface
  • Aggregated reviews backend server 102 collects product reviews from multiple Web sites 108-110, identifies particular products that are associated with particular product reviews, generates aggregated review information for particular products, and stores the product reviews and the aggregated review information.
  • Aggregated reviews frontend server 100 receives and responds to requests from client 104 to provide an aggregated review for a product and/or to search within reviews for a particular product.
  • GUI 112 includes portions of a plurality of reviews for a product and a search input area for entering search terms to search for reviews of the product that contain the search terms.
  • Figure 2A is a block diagram illustrating aggregated reviews backend server
  • Server 102 typically includes one or more processing units (CPU's) 202, one or more network or other communications interfaces 204, memory 206, and one or more communication buses 214 for interconnecting these components.
  • Server 102 optionally may include a user interface 208 comprising a display device 210 and a keyboard 212.
  • Memory 206 may include high speed random access memory and may also include non-volatile memory, such as one or more magnetic disk storage devices. Memory 206 may optionally include one or more storage devices remotely located from the CPU(s) 202.
  • the memory 206 stores the following programs, modules and data structures, or a subset or superset thereof: • an operating system 216 that includes procedures for handling various basic system services and for performing hardware dependent tasks; • a communication module 218 that is used for connecting server 102 to other computers (e.g., Web sites 108 and 110) via one or more communication interfaces 204 (wired or wireless), which in turn connect the backend server 102 to one or more communications networks such as the Internet, other wide area networks, local area networks, metropolitan area networks, and so on;
  • an operating system 216 that includes procedures for handling various basic system services and for performing hardware dependent tasks
  • a communication module 218 that is used for connecting server 102 to other computers (e.g., Web sites 108 and 110) via one or more communication interfaces 204 (wired or wireless), which in turn connect the backend server 102 to one or more communications networks such as the Internet, other wide area networks, local area networks, metropolitan area networks, and so on;
  • a crawling module 220 for crawling Web sites (e.g., 108 and 110), which includes o a URL queue 222 that stores URLs to be crawled by crawler 226; o a link parser 224 that tells crawler 226 how to extract new links to follow from a fetched page; o a crawler 226 that fetches web pages, and o a fetched pages buffer 228 that temporarily stores web pages fetched by crawler 226;
  • a review extraction module 230 for extracting product reviews from the information collected by crawling module 220, which includes a review parser 232 that extracts content pertaining to a review (e.g., the review text, author, and date), as well as content that identifies the product that the review is about (e.g. the product name, model number, and/or other product identifiers), from the fetched pages in buffer 228;
  • a review parser 232 that extracts content pertaining to a review (e.g., the review text, author, and date), as well as content that identifies the product that the review is about (e.g. the product name, model number, and/or other product identifiers), from the fetched pages in buffer 228;
  • an extracted reviews buffer 234 that temporarily stores the reviews extracted by review parser 232
  • a review aggregating module 236 that identifies particular products that are associated with particular product reviews and generates aggregated review information for particular products
  • an aggregated review buffer 240 that stores aggregated review information for a product, such as the total number of reviews for the product, an average rating for the product, a distribution of ratings, and/or frequently appearing phrases in the extracted product reviews associated with the product; one or more additional types of aggregated review information may be stored in the aggregated review buffer 240 in other embodiments;
  • a review database 242 that stores individual reviews 244 and aggregated reviews 246; • a review indexer 248 that indexes the reviews (244 and 246) stored in the review database 242 so as produce a reviews index 250; and
  • the reviews index 250 maps terms (e.g., words and phrases) to reviews. In some embodiments, the reviews index 250 also maps other values, such as ClusterIDs (i.e., product identifiers) or review author names, to the corresponding reviews.
  • ClusterIDs i.e., product identifiers
  • review author names i.e., review author names
  • Figure 2B is a block diagram illustrating aggregated reviews frontend server
  • the memory 276 in frontend server 100 stores the following programs, modules and data structures, or a subset or superset thereof:
  • a product database 252 for storing product-related information, which includes o product and vendor information 254 such as product names, models, categories, and purchasing information; and o a product-to-review map 256 that associates products in database 252 with their corresponding ClusterIDs (i.e., product identifiers); the ClusterIDs are mapped by the reviews index 250 to individual reviews 244 and aggregated reviews 246;
  • a review search module 258 that answers search requests, which includes o a "search all reviews” application 260 for searching all reviews in reviews database 242; and o a "search within reviews for a product” application 262 for searching within the set of reviews for a particular product, to identify those reviews containing one or more search terms; and
  • a presentation module 264 for formatting aggregated reviews and search results for display, including o a reviews formatter 266 for formatting individual reviews and aggregated review information for display; and o a reviews sorter 268 for sorting the reviews for a particular product in any of the following ways, or a subset or superset thereof: by date, review source ( e -g-j by website), review quality, or review relevance to one or more search terms.
  • the reviews database 242 and reviews index 250 are generated by the backend server 102, and are copied from the backend server to the frontend server 100 for responding to user requests for product review information.
  • each of the above identified modules and applications corresponds to a set of instructions for performing a function described above. These modules (i.e., sets of instructions) need not be implemented as separate software programs, procedures or modules, and thus various subsets of these modules may be combined or otherwise re-arranged in various embodiments.
  • memory 206 or 276 may store a subset of the modules and data structures identified above.
  • memory 206 or 276 may store additional modules and data structures not described above.
  • Figures 2A and 2B show servers 100 and 102 as a number of discrete items
  • Figures 2A and 2B are intended more as functional descriptions of the various features which may be present in servers 100 and 102 rather than as a structural schematic of the embodiments described herein.
  • items shown separately could be combined and some items could be separated.
  • some items shown separately in Figures 2A and 2B could be implemented on single servers and single items could be implemented by one or more servers.
  • the actual number of servers used to implement backend server 102 and frontend server 100 and how features are allocated among them will vary from one implementation to another, and may depend in part on the amount of data traffic that the system must handle during peak usage periods as well as during average usage periods.
  • Figure 3 is a flowchart representing a method for collecting and aggregating reviews for a product according to one embodiment. The process shown in Figure 3 is performed by aggregated reviews backend server 102 ( Figures 1 and 2A). It will be appreciated by those of ordinary skill in the art that one or more of the acts described may be performed by hardware, software, or a combination thereof, as may be embodied in one or more computing systems.
  • Backend server 102 collects product reviews, or information containing product reviews for a plurality of products (302).
  • collecting information comprises selectively crawling review-related Web sites or portions thereof and retrieving information containing product reviews.
  • Selective crawling differs from traditional crawling in that the crawler only follows selected links on Web pages, rather than all links.
  • crawling module 220 To initiate a crawl, crawling module 220 is given a set of starting (sometimes called “seed") URLs (Uniform Resource Locators, i.e., web addresses) to crawl.
  • seed Uniform Resource Locators, i.e., web addresses
  • Crawling module 220 puts these URLs in queue 222.
  • Crawler 226 continuously fetches pages corresponding to the URLs in the queue 222 until either there more no more URLs in the queue 222, or a crawl termination condition is reached.
  • link parser 224 tells crawler 226 how to extract new links to follow from a fetched page by matching regular expression patterns.
  • link parser 224 tells crawler 226 how to extract new links to follow from a fetched page by using text classification techniques.
  • Table 1 contains pseudo code for a link parser 224 that selects which links to follow on an exemplary website (www.productcom.pany. com) for reviews of electronics products.
  • Crawler 226 outputs the crawled pages to fetched pages buffer 228.
  • the fetched pages buffer may contain one or more files or other data structures.
  • link parser 224 parses the page and attempts to extract links (URLs) to follow that, if found, are added to queue 222.
  • URLs links
  • parser 224 may identify such links based on the presence of terms or patterns in the URLs of the links, or based on the anchor text of the links.
  • Anchor text is text contained in or otherwise associated with a link.
  • the information containing product reviews can be collected from a wide variety of sources, including web pages and forum postings.
  • the web pages comprise Web pages of review-related Web sites and/or product-related Web sites.
  • collecting information comprises receiving product reviews in a predetermined digital format, for example as data streams from multiple product reviewers.
  • the predetermined digital format of the product reviews may comprise key/value pairs, or data organized in predefined data structures.
  • collecting information comprises selecting information from a repository of information collected by a web crawler.
  • Backend server 102 automatically extracts product reviews from the collected information (304).
  • review parser 232 which is analogous to link parser 224, extracts content pertaining to a review (e.g., the review text, author, and date), as well as content that identifies the product that the review is about (e.g. the product name, model number, or other features), from the fetched pages in buffer 228.
  • the extracted information is stored in extracted reviews buffer 234.
  • extracted reviews buffer 234 contains a list of key /value pairs denoting the extracted product review information, such as the review author, content, publisher, and so on.
  • each extracted review found on a web page is encoded and stored in a respective review record in the extracted reviews buffer 234.
  • Table 2 lists the fields of each review record in the extracted reviews buffer 234 in an exemplary embodiment.
  • each review record may have a subset of the fields listed in Table 2, and may also include additional fields.
  • the review records might not include the ReviewType and CrawlDate fields.
  • the Rating field may be a numeric field, instead of a string field.
  • each review record may include both a string Rating field and a normalized numeric rating field (e.g., normalized with respect to a rating scale of 0 to 1, or 0 to 5).
  • the keyword “required” indicates a required field
  • “repeated” indicates a field that may occur more than once in a record (and in rare cases, may have zero occurrences)
  • “optional” indicates an optional field.
  • Optional fields may be present in some review records, while not present in other review records, for example because some reviews may not include the optional information.
  • Exemplary Fields for Individual Review Records in Individual Reviews Buffer and Reviews Database required int64 ReviewID // unique identifier for each review repeated string
  • optional int64 ClusterID // ClusterID of the product optional float QualityScore // Quality Score of the review optional int32 Year // Model year, or other year associated with the product // (e.g., year of first publication of a book or a movie)
  • Quality S core and ClusterlD are filled with data from the extracted review information if information relevant to those fields are found in the extracted review information.
  • ReviewID is a unique identifier assigned to each individual review by the aggregated reviews backend server.
  • the ReviewType indicates whether the review is an editorial review, or a review submitted by a user.
  • Quality S core which provides a measure of the quality of a review, is determined based on the review's length and word entropy (which measures, among other things, the diversity of the words or terms used in the review). As explained below, all reviews belonging to the same product form a "cluster" and the ClusterlD identifies the cluster to which a review belongs.
  • Backend server 102 automatically identifies particular products that are associated with particular product reviews (306). For at least some of the extracted product reviews, backend server 102 identifies a particular product that is associated with the extracted product review.
  • identifying a particular product that is associated with the extracted product review comprises associating a unique number or string in the extracted product review with a particular product.
  • the unique number or string is a product model number.
  • the unique number is an ISBN number.
  • identifying a particular product that is associated with the extracted product review comprises associating a brand name and a model number in the extracted product review with a particular product. In some embodiments, identifying a particular product that is associated with the extracted product review comprises associating a brand name, a model number, and a product category in the extracted product review with a particular product.
  • identifying a particular product that is associated with the extracted product review is simple if the product review includes the global identifier.
  • extracted brand names and model numbers in the above example, the brand name is "canon", and the model numbers are "lide30" and "7890a002"
  • category information such as the fact that the above product is a scanner
  • reviews about the same product form a "cluster” and “clustering” refers to identifying reviews for the same product.
  • this information is stored in review records (Table 2) by review extraction module 230.
  • Extracted brand names, model numbers and category information may all be stored in the ProductNames fields of the review records.
  • each review record may include a Model field for storing a model number, a Category field for storing product category information, and a Names field for storing brand name information for a respective product.
  • Model, Category and Brand Name are not explicitly identified on a web page.
  • this information is deduced from the title string or other fields of the extracted review.
  • product category and model number can be inferred by the review parser 232 or the review aggregating module 236 as follows. Given a title string, the part of the title string beyond words like "for", “with”, and “featuring” is ignored. Words in the title string are traversed from right to left and the first word (or pair of words) that is included in a data file containing a predefined list of product categories is considered the product category.
  • this data file also identifies, for each distinct product category, a list of other product category names for the product category that are mapped to one canonical name. For example, “notebooks”, “notebook”, “laptop” or “laptops” may all be mapped to the canonical name, “notebooks”. Thus, a title string such as "Canon canoscan lide 30 color scanner with free extras” would be mapped to the category “scanner”.
  • model numbers the title string is traversed from left to right and the first word that contains digits (i.e., characters from '0'-'9') is concatenated with the preceding word to form a model number.
  • the resulting model number is discarded if it matches any of the regular expression patterns corresponding to blacklisted model numbers or contains a brand name or parts of a brand name.
  • An example of a blacklisted pattern is "pentium ⁇ d+" +" (i.e., "pentium” followed by a string of digits).
  • "lide 30" is extracted as the model number. Note that more than one model number can be inferred for a product name.
  • the brand names are identified by matching words in the product title with known (i.e., predefined) brand names in a list or dictionary of such brand names.
  • the brand name of a product may be extracted from the URL of the page on which the product review is found.
  • two product reviews are assigned to the same cluster
  • reviews corresponding to products for which brand or model information cannot be determined are put into a singleton cluster of their own and are not clustered with any other reviews.
  • review aggregating module 236 fills in the ClusterID field in the review record for the extracted review.
  • backend server 102 For each particular product in at least a subset of the plurality of products, backend server 102 generates aggregated review information for the particular product based on a plurality of extracted product reviews that are associated with the particular product (308).
  • review aggregating module 236 uses the reviews for a particular product to generate aggregated review information for the product that is stored in aggregated review buffer 240.
  • the aggregated review information for the particular product comprises a total number of reviews for the product, an average rating for the product, a distribution of the ratings for the product, and/or frequently appearing phrases in the extracted product reviews associated with the product.
  • One or more additional types of aggregated review information may be included in the aggregated review information in other embodiments.
  • the average rating is the average of weighted ratings from different web sites.
  • the weight of a single review on a web site with K reviews for the same product is 1/K.
  • the weight given to reviews from a particular web site or reviewer can be personalized by a user. For example, a user can specify via a preferences dialog box one or more web sites (e.g., www.consumerreports.org) and/or reviewers (e.g., Roger Ebert) preferred by the user.
  • the ratings given in the reviews associated with the user's preferred sources are then given higher weight when computing an average rating for a product.
  • the ratings given in reviews associated with the user's preferred sources are given N times the default weight assigned to those reviews, where N is a value such as 2, 2.5, 3 or any other appropriate value greater than 1.
  • An exemplary representation of the average rating computation is:
  • frequently appearing phrases in the extracted product reviews associated with a particular product are computed by review aggregation module 236 as follows. For all the phrases (e.g., unigrams, bigrams or trigrams that do not span across sentences) that occur in reviews associated with a product (cluster), the number of their weighted occurrences is counted. Occurrences in the review title or summary are given a higher weight. When computing or selecting frequently appearing phrases, all stop words and words that occur in the title of the product name are ignored.
  • Each phrase is given a score equal to the length of the phrase times the square root of the sum of weighted occurrences of the phrase.
  • a predetermined number of phrases with the highest scores e.g., the top 6, 7, 8, 9 or 10 phrases
  • Table 3 lists the fields of each aggregated review record, produced by aggregating information from all the extracted reviews for a particular product, and stored in the aggregated reviews buffer 240.
  • each aggregated review record may have a subset of the fields listed in Table 3, and may also include additional fields.
  • a separate instance of the aggregated review record is included in the aggregated reviews buffer 240 and in the reviews database 242 for each distinct product.
  • each aggregated review record includes a ClusterID (identifying the product associated with this record), a list of ReviewID's (each of which is uniquely associated with a review of the product), a list of the words or phrases most frequently found in the reviews of the product, and a computed average rating of all the reviews for the product.
  • an aggregated review record also includes one or more of the following additional fields: a string identifying the product, a number specifying the number of reviews in the cluster; and data concerning the distribution of ratings in the reviews for a product, such as the number of reviews with low, medium and high ratings, as well as the number of reviews having no product rating.
  • Optional Fields that may be included in some embodiments: optional int32 ReviewNum // number of reviews in cluster repeated string ProductName // name(s) of Product to which this cluster belongs repeated int32 ScoreDistribution // score distribution for: unrated, negative, neutral,
  • Backend server 102 automatically stores the extracted product reviews 244 and the aggregated review information 246 in reviews database 242 (310).
  • each stored product review in the reviews database 242 comprises a record that includes the same information as the corresponding review record (Table 2) stored in the extracted reviews buffer 234, while in other embodiments, the stored product review may comprise a subset of the information stored in the corresponding review record.
  • reviews indexer 248 indexes the individual product reviews 244 and the aggregated review information for a product 246 and stores the results in reviews index 250. Displaying an aggregated review
  • Client 104 sends a request for an aggregated review for a product (402,
  • the request is sent in response to a user-initiated activity, such as a user clicking on a hyperlink.
  • a user-initiated activity such as a user clicking on a hyperlink.
  • a user looking at web page 650 with various products for sale could activate hyperlink 652 to see an aggregated review for product 654.
  • frontend server 100 receives the request (404) for an aggregated review for a product and obtains (406) the aggregated review.
  • product information 254 in product database 252 is linked to aggregated reviews for the corresponding products by product-to-review map 256.
  • map 256 contains mappings of product identifiers used by a first website or service (e.g., a product information website) to the ClusterIDs used by the frontend server 100.
  • the process of assigning product identifiers to products in the first website or service may be analogous to the process described above for identifying particular products that are associated with particular product reviews.
  • map 256 is determined by matching a global identifier for a particular product (e.g., an ISBN number) in product database 252 with a corresponding review cluster.
  • Product-to-review map 256 is used to identify the aggregated review (in the reviews database 242) corresponding to a product.
  • Presentation module 264 formats the aggregated review.
  • the reviews index 250 is used to identify the individual reviews corresponding to the aggregated review for a product. More specifically, the reviews index 250 maps the ClusterID of the aggregated review to the individual reviews for the product associated with the aggregated review.
  • Frontend server 100 sends (408) the aggregated review to client computer 104 via communications network 106.
  • Client 104 receives (410) and displays (412) the aggregated review 600 ( Figure 6B).
  • aggregated review 600 includes portions of reviews
  • aggregated review 600 includes a histogram 608 of review ratings for the product, an aggregate rating 604 for the product, and/or the number 606 of reviews for the product. In some embodiments, aggregated review 600 is displayed in a single Web page. Searching
  • review search module 258 supports two different types of queries: (1) search within the reviews 262 belonging to a single product (e.g. a search for "customer service” in the reviews for a given product) and (2) search all reviews 260 to find a particular product (e.g. a search for "good digital camera”).
  • Aggregated review 600, Figure 6B shows a search input area 618 for the first type of search.
  • a variety of user initiated activities can start the search process, including the user entering search terms in search input area 618, selecting a hyperlink for a frequently appearing phrase 620, selecting a hyperlink to a review source 622, or selecting a hyperlinked portion (e.g., 610, 612, 614, or 616) of histogram 608.
  • client 104 sends (502, Figure 5) a request to search within a plurality of reviews for a particular product.
  • frontend server 100 receives (504) the search request from client 104 and obtains (506) information satisfying the request.
  • the information is obtained by review search module 258 sending two queries to reviews database 242.
  • the first query all individual reviews (sometimes called "documents") that contain the search terms are found.
  • the first query is restricted to the main body (e.g., the "review content” fields) of the reviews index 250, and only to individual reviews that belong to the product (cluster).
  • a second query is then issued to obtain portions of all reviews that are to be displayed.
  • the ordering preferences submitted by the user e.g., by clicking on 636, 638, 640, and/or 642
  • the start position indicates the ordinal number within an ordered set of reviews at which to begin formatting reviews for display.
  • Frontend server 100 sends (508, Figure 5) at least a portion of the obtained information to client 104.
  • Client 104 receives (510) information satisfying the request and displays (512) the received information, e.g. in web page 660, Figure 6C. In some embodiments, Client 104 displays at least portions 662 of reviews of the product matching the search terms 664.
  • the information sent by frontend server 100 and received by client 104 includes at least portions 662 of reviews for the particular product that satisfy the search request. In some embodiments, this information includes the identities 666 of the review sources contributing to the sent information.
  • the information is also obtained by review search module 258 sending two queries to reviews database 242.
  • first query all individual reviews that contain the search terms are found, without restricting the search to a particular product (cluster).
  • the results of the first query are grouped by cluster (product).
  • a second query is then issued to obtain portions of at most three (or some other small number) reviews in each cluster, which are restricted to be from different publishers.
  • information about each of the clusters such as the total number of reviews and the average rating, is also requested, obtained, and sent for display.
  • the graphical user interface 600 ( Figure 6B) on client computer 104 comprises a plurality of portions 626 of reviews for a product and a search input area 618 for entering search terms to search for reviews of the product that contain the search terms.
  • the plurality of portions of reviews can be grouped by review source 636, sorted by date 642, and sorted by relevance to the search input 640, or sorted by quality of review when the user has not provided a search input.
  • more or fewer sorting and grouping options may be provided for sorting and/or grouping the reviews.
  • graphical user interface 600 also includes, either alone or in various combinations: an aggregate rating 604 for the product; a list of suggested search terms 620; a list of sources 622 for the reviews that link back to the source Web sites or to the corresponding reviews; at least one user selectable link for sorting the portions of reviews by date 642 or relevance to the search input 640, and/or for grouping the portions of reviews by source 636; and/or a histogram 608 of reviews for the product.
  • clicking on a region e.g., 610, 612, 614, or 616) in the histogram automatically generates portions of at least some of the reviews corresponding to the histogram region. For example, clicking on region 616 may generate a new display showing portions of reviews having high ratings for the product.
  • An aggregated rating 604 is given to the product by calculating an average over all reviews for the product.
  • the ratings in the individual reviews 244 and in the aggregated reviews 246 are normalized, e.g., to a 0-1 or 0-5 scale.
  • the aggregated rating 604 and number 606 of reviews are changed to reflect the average rating and number of reviews matching the search. This change in the aggregated rating can help users answer questions like, "How do reviewers that discuss 'customer service' rate this product?"
  • the aggregated rating 604 and number 606 of reviews are not changed to reflect the average rating and number of reviews matching the search.
  • the list of review sources 622 includes links to the sites from which reviews for the product have been obtained, or a subset thereof. In some embodiments, after a search within reviews for a product, the list of review sources 622 just includes links to the sites from which reviews matching the search have been obtained, or a subset thereof. In some embodiments, the links lead to the main pages for the given product on each source web site. In some embodiments, the links lead to the product reviews matching the search.
  • a histogram 608 with a rating distribution of the reviews includes the number of reviews that are unrated 610 (in some embodiments, displayed as a gray area), negative 612 (in some embodiments, displayed as a red area), neutral 614 (in some embodiments, displayed as a yellow area) and positive 616 (in some embodiments, displayed as a green area).
  • the rating distribution is displayed as a flat histogram, where the length of each colored bar is proportional to the number of reviews in the category, Clicking on a histogram results in a search 262 that just shows the corresponding reviews, e.g. clicking on the red bar 612 results in showing just the negative reviews.
  • Search input area 618 allows a user to enter arbitrary search terms for the search within reviews for a given product. As shown in Figure 6C, in some embodiments the search terms are highlighted in portions of reviews 626 that match the search query.
  • User selectable links permit the displayed list of reviews to be sorted in various ways, such as by:
  • Relevance 640 In some embodiments, editorial reviews are listed before user reviews. In some embodiments, the reviews are sorted by their length and information content, e.g., sorted by Review-per- word-entropy * sqrt(Re view-Length). Other scoring functions, such as scoring reviews by an "information retrieval" score, can also be used.
  • Date 642 The reviews are sorted by date, with the newest reviews coming first. Reviews for which a date is unknown may be listed at the end.
  • Review Source 636 Reviews are grouped by review site, as represented by screen shot 670 in Figure 6D, with at most N reviews (e.g., 3, 4 or 5, or some other small number) shown for each review site. At the top of each group, a link 672 to the corresponding review site is provided, which may be the same as the link provided to the site in the list of review sources 622.
  • N reviews e.g. 3, 4 or 5, or some other small number
  • Ungrouped 638 (e.g., not grouped by review source). This view is similar to traditional search result displays. Reviews are listed (i.e., portions of the reviews are displayed) one-by-one in a selected order (e.g., by date or relevance) and reviews from different sources are mixed (i.e., without grouping the portions of reviews by source).
  • Location (not shown in aggregated review 600, Figure 6B). This view permits reviews to be sorted by location (e.g., for reviews of restaurants, local businesses, moyies or other products near a particular location, such as by address, city, region, or mailing or zip code).
  • a portion 626 of the review e.g., a snippet
  • the portion or snippet 626 of each review may be generated using the same or similar techniques to those used to generate snippets of search results in an Internet search engine such as the Google search engine.
  • the portion of each review shown may be a portion matching one or more search terms that were used to select the reviews to be displayed.
  • the portion of each review shown may be a beginning portion of the review.
  • the portion of each review shown may be a portion determined by an automatic process to be a summary or most important portion of the review.

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  • Business, Economics & Management (AREA)
  • Strategic Management (AREA)
  • Engineering & Computer Science (AREA)
  • Accounting & Taxation (AREA)
  • Development Economics (AREA)
  • Finance (AREA)
  • Economics (AREA)
  • Game Theory and Decision Science (AREA)
  • Entrepreneurship & Innovation (AREA)
  • Marketing (AREA)
  • Physics & Mathematics (AREA)
  • General Business, Economics & Management (AREA)
  • General Physics & Mathematics (AREA)
  • Theoretical Computer Science (AREA)
  • Information Retrieval, Db Structures And Fs Structures Therefor (AREA)
  • User Interface Of Digital Computer (AREA)

Abstract

Les modes de réalisation de l'invention concernent des nouveaux moyens plus efficaces permettant d'obtenir des avis de produits sur Internet, de regrouper des avis pour un même produit, et de proposer à des utilisateurs finaux un avis regroupant plusieurs avis dans un format pouvant faire l'objet d'une recherche. Dans un aspect de l'invention, une interface utilisateur graphique utilisée sur un ordinateur comprend une pluralité de parties d'avis pour un produit et une zone d'entrée de recherche qui permet de saisir des termes de recherche afin de rechercher des avis du produit contenant lesdits termes de recherche.
EP05848898A 2004-12-14 2005-11-30 Procede, systeme et interface utilisateur graphique permettant de fournir des avis concernant un produit Withdrawn EP1834249A4 (fr)

Applications Claiming Priority (3)

Application Number Priority Date Filing Date Title
US11/012,846 US20060143158A1 (en) 2004-12-14 2004-12-14 Method, system and graphical user interface for providing reviews for a product
US11/012,500 US7962461B2 (en) 2004-12-14 2004-12-14 Method and system for finding and aggregating reviews for a product
PCT/US2005/043459 WO2006065546A2 (fr) 2004-12-14 2005-11-30 Procede, systeme et interface utilisateur graphique permettant de fournir des avis concernant un produit

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EP1834249A4 EP1834249A4 (fr) 2009-12-09

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Cited By (3)

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EP1949332A2 (fr) * 2005-09-30 2008-07-30 Google Inc. Selection d'analyses de haute qualite a presenter
EP1949332A4 (fr) * 2005-09-30 2011-08-24 Google Inc Selection d'analyses de haute qualite a presenter
US8438469B1 (en) 2005-09-30 2013-05-07 Google Inc. Embedded review and rating information

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EP1834249A4 (fr) 2009-12-09
CA2591441A1 (fr) 2006-06-22
WO2006065546A2 (fr) 2006-06-22
WO2006065546A3 (fr) 2007-11-15

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