KR20080096887A - Ranking system based on user's attention and the method thereof - Google Patents
Ranking system based on user's attention and the method thereof Download PDFInfo
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- KR20080096887A KR20080096887A KR1020070041741A KR20070041741A KR20080096887A KR 20080096887 A KR20080096887 A KR 20080096887A KR 1020070041741 A KR1020070041741 A KR 1020070041741A KR 20070041741 A KR20070041741 A KR 20070041741A KR 20080096887 A KR20080096887 A KR 20080096887A
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- G06F16/90—Details of database functions independent of the retrieved data types
- G06F16/95—Retrieval from the web
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Abstract
The present invention relates to an information retrieval ranking system and method that reflects user interest.
The information retrieval ranking system disclosed by the present invention comprises an UAL collection module for collecting a plurality of user behavior logs (UAL) from a user terminal or a network provider server connected to an information communication network, and the document DB based on the collected user behavior logs. For all users gathered in the individual documents accumulated in, the interest rank (AR) is calculated from the product of the interest value (AV) and the user's behavior influence (IV) for all actions shown by each user, and is calculated in the interest rank DB. A rank calculation module for updating the rank of interest and a rank value for calculating and ranking a rank value (RV) from the rank of interest of interest rank DB for each document retrieved from the document DB by receiving a query-based search request from a user terminal. It includes a calculation module.
According to the present invention, by converting the behavior shown in the individual document to the interest and then assigning it to the document and applying it to the ranking, it is possible to provide the user with improved search results.
Description
1 is an exemplary view showing an entire system to which the technical spirit of the present invention is applied;
2 is a block diagram of an information retrieval ranking system according to the present invention;
3 is a detailed configuration diagram of an interest rank calculation module according to the present invention;
4 is a flowchart illustrating an information retrieval ranking method according to the present invention.
** Description of symbols for the main parts of the drawing **
200: information search ranking system
210: document collection module 220: search engine
230: UAL collection module 240: interest rank calculation module
242: interest value calculator 244: behavior influence calculator
250: rank value calculation module 10: document DB
20: UAL DB 30: Rank of interest DB
The present invention relates to a technique of calculating a user's interest in a document based on a user action log (UAL) and reflecting the same in a ranking of search results.
As is well known, the information retrieval system indexes documents corresponding to a query input by a user and generates search results. The documents included in the search results are provided in a ranked list form (set of links pointing to the documents) through statistical techniques such as content analysis and link analysis.
Here, 'document' is used in a somewhat vague sense, but can be generally defined as a web page. Documents such as web pages are largely composed of content and metadata, which form a comprehensive meaning that includes text, voice, and video files. The metadata may include document language, document title, document size, document identifier (eg, URL information), document format, category, and various other attributes.
On the other hand, the ranking of information retrieval mainly uses the content and metadata of the document, and the relationship information (eg, links or categories) between the documents. However, such information is described from the point of view of a provider who wants to generate or distribute the content, and does not reflect the viewpoint of the consumer who ultimately consumes the content. For example, information on the user's point of view, such as content that is noticeable or currently popular, is excluded from the ranking decision factor, while elements of the provider's position such as a title or a backlink in the document are employed.
A good example of an information retrieval system is Google. In the case of Google, based on the 'PageRank' technique, the link information (hyperlink, hyperlink) pointing to the document is analyzed in addition to the information included in the document, and the page rank value is assigned to the document. The information and the previously given page rank value (range of 0 to 10) are collected to provide a ranked search result. This technique can be well realized in websites where link information is the main factor.
However, user creation contents (UCC) and mobile contents, such as video contents and blog contents, which are rapidly increasing in recent years, lack link information. Therefore, a page rank technique cannot expect desirable search results.
The present invention converts the behaviors shown in individual documents (contents) into interests and assigns them to the corresponding documents to apply them to ranking, thereby providing the user with improved search results.
The information retrieval ranking method of the present invention for achieving the technical problem is basically applied to a system including a search engine that searches for documents accumulated in the document DB according to a search request and provides ranked search results.
According to a feature of the present invention, a process of collecting and accumulating a plurality of documents in a document DB through an information communication network, a process of collecting and accumulating user behavior logs (UAL) from a user terminal or a network provider server, and collected user behaviors The interest rank DB is calculated by calculating the interest rank (AR) from the product of the interest value (AV) and the behavior influence (IV) of all actions shown by each user for all users gathered in the individual documents accumulated based on the logs. Process of accumulating the rank value (RV) from the interest rank of the interest rank DB for each document retrieved from the document DB by receiving a query-based search request from the user terminal and providing a search result ranked by the user terminal. Is done.
The interest value AV of the present invention is a predetermined weight given to each action performed by a user gathered in an individual document.
) Is calculated as the product of the sigmoid function whose behavior elapsed time t is a variable.Also, the behavioral influence (IV)
Calculate as Is the sigmoid function, Is the total number of all actions performed by user h, and m is Divided by the number of users.Specific features and advantages of the present invention will become more apparent from the following detailed description based on the accompanying drawings. In the meantime, when it is determined that the detailed description of the known functions and configurations related to the present invention may unnecessarily obscure the subject matter of the present invention, it should be noted that the detailed description is omitted.
1 is a diagram illustrating an entire system to which the present invention is applied, and a user action log (UAL) for a document that is viewed and viewed by one or more contents (document) servers not shown linked to an information communication network. : Collects and indexes a plurality of documents through a document user terminal 100 generating a user action log) and an information communication network, and collects user activity logs from the document user terminal or from a network provider server (not shown). After calculating an Attention Value (AV) based on the collected user behavior log, an information
In the present invention, the user terminal includes a mobile phone or computer capable of internet communication, and is divided into a 'document user terminal' and a 'document search user terminal' for convenience of description. 'Document User Terminal' is based on the aspect of reading documents (e.g. web surfing) from a number of content servers connected to the information communication network and generating a log of user actions (UAL) accordingly. 'Accumulates interest ranks for each document based on a plurality of user activity logs collected by the information retrieval
On the other hand, the user action log (UAL) is a log file that records the user actions (user actions) that occur when a document user terminal reads a document, 1. document identifier, 2. action identifier, 3. The type of action, 4. time of action, and 5. additional data.
In more detail, the document identifier is an identifier for a document that is the target of an action. The 'action identifier' is information for identifying the user who caused the action, for example, may be an IP address or a MAC address when the user uses a computer, and may be a mobile phone number or unique mobile phone information when using a mobile phone.
In addition, the 'type of behavior' refers to the act of reading (looking at the summary of documents, previews, etc.), playing (looking at videos, music, images, etc.), viewing details (seeing the full text of the document, not summary information). ), Archiving (keeping or storing documents), making a purchase (purchasing documents or content for sale), making recommendations (recommending documents or content to others), evaluating (individual Expressing opinions as digitized or regularized information), attaching additional information (giving additional information such as comments or tags to documents or contents), bookmarking (storing or storing addresses for later viewing of documents or contents) And the like's behavior. Meanwhile, the 'additional data' may include, for example, a user's location and an environment type (for example, moving) as environment information of an action performed by a user.
2 is a detailed configuration diagram of the information
The
The
In the present invention, 'user interest' means a numerical value by applying a user's memory model (or memory model) as a main factor for document (content) evaluation. As is well known, humans recognize a fact or object and then gradually forget it over time. This is the human memory model represented by the sigmoid function. Conventionally, in order to quantify that the user's attention is focused on a document, the placenta is based on link information (Google's PageRank) or the total number of times the document is viewed. However, these indicators are merely cumulative indicators that do not reflect the temporal component of the memory model.
Meanwhile, as mentioned above, the
The interest
.................. Equation 1
In Equation 1, the interest value AV calculated by the
............................... [ Equation 2 ]
here,
Means that user h showed k action on document p. Is a predetermined weight given to the type of action k, and t is the difference between the time at which the interest value AV is calculated and the time k is actually occurring, i.e., the progress of the action k. It means time (hereinafter, elapsed time of action).The weight has a value between 0 and 1 depending on the type of action. For example, 0.2 for 'read', 0.4 for 'play', 0.5 for 'detail',... For example, 'recommended' may be defined as 0.9. Equation 2 is a sigmoid function whose behavior elapsed time t is a variable as defined on the right side, and models the aforementioned human memory. Thus weight
Decreases with the elapsed time of the act.In the interest value AV calculated by the
............................... [ Equation 3 ]
here,
Represents the sigmoid function, Is the total number of all actions shown by user h, and m is the above Divided by all users ( ). Equation 3 may be summarized as 'a standard deviation of a user's behavior'.Meanwhile, when the
.......... [Equation 4]
Here, DocRel means predetermined document similarity with respect to the query word. Since the calculation of the sentence similarity has been disclosed in a number of cases implemented by various algorithms, detailed description thereof will be omitted.
Hereinafter, the information retrieval ranking method according to the above-described preferred embodiment will be described with reference to FIG. 4. First, the
Subsequently, the
The interest
For the query-based search request from the document search user terminal 300, the
Subsequently, the
According to the present invention as described above, by using the behavior shown in the individual document as the user interest based on the human memory model, it is possible to provide a high-quality ranked search results sensitive to the user's interest or preference for the query-based search request. Can be. In addition, it is possible to provide a high-quality ranking search results for the user's work (UCC) that has recently increased rapidly.
Although the embodiments of the present invention have been described in detail above, the scope of the present invention is not limited thereto, and various modifications and improvements of those skilled in the art using the basic concepts of the present invention defined in the following claims are also provided. It belongs to the scope of rights.
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KR1020070041741A KR100923505B1 (en) | 2007-04-30 | 2007-04-30 | Ranking system based on user's attention and the method thereof |
PCT/KR2007/002701 WO2008133368A1 (en) | 2007-04-30 | 2007-06-04 | Information search ranking system and method based on users' attention levels |
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