Self-adaptive personalized information retrieval system and method

An information retrieval and self-adaptive technology, applied in special data processing applications, instruments, electrical and digital data processing, etc., can solve problems such as fixation and influence query, and achieve high flexibility, high retrieval accuracy, and flexibility. and the effect of rationality

Active Publication Date: 2012-11-14
哈尔滨工业大学高新技术开发总公司
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  • Abstract
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  • Application Information

AI Technical Summary

Problems solved by technology

[0004] The aforementioned personalized retrieval models based on user interests have a significant disadvantage: once the model is trained, the internal parameters of the model are all fixed values, which are relatively fixed.
In other words, the user's interaction with the search engine will affect the next query submitted by the user.

Method used

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  • Self-adaptive personalized information retrieval system and method
  • Self-adaptive personalized information retrieval system and method
  • Self-adaptive personalized information retrieval system and method

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Experimental program
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specific Embodiment approach 1

[0074] Specific Embodiments 1. The self-adaptive personalized information retrieval system described in this embodiment includes:

[0075] It is used to form a feature matrix based on the current query information, combined with historical query information and historical click information, and is also used to obtain the data input subsystem of the training parameter prediction model according to the feature matrix;

[0076] A parameter training and prediction subsystem for training and applying a parameter prediction model based on the feature matrix, and obtaining prediction parameters;

[0077] It is used to organize current queries, historical queries and historical clicks with predicted parameters; it is also used to combine user models and query models to form a personalized query model execution retrieval subsystem;

[0078] It is used to find the documents that match the personalized query in the documents to be retrieved as the preliminary retrieval results, and is al...

specific Embodiment approach 2

[0079] Embodiment 2. This embodiment is a further limitation of the data input subsystem in the self-adaptive personalized information retrieval system described in Embodiment 1. The data input subsystem in this embodiment includes:

[0080] a module for generating user behavior features based on current query information, and

[0081] A module for constructing a feature matrix based on all the obtained user behavior features.

specific Embodiment approach 3

[0082] Specific Embodiment 3. This embodiment is a further limitation of the parameter training and prediction subsystem in the self-adaptive personalized information retrieval system described in Embodiment 1. In this embodiment, the parameter training and prediction subsystem includes:

[0083] A data input module for receiving data to be processed;

[0084] A module for calculating historical queries and historical clicks corresponding to each query, and organizing them into the required data format;

[0085] modules used to form feature matrices;

[0086] A module for finding the optimal parameter of the current query in a traversal search mode, the step size of the traversal is 0.1;

[0087] A module for building a mapping of user characteristics and optimal parameters using an SVM regression model.

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Abstract

The invention discloses a self-adaptive personalized information retrieval system and method. For timely catching irregularly distributed dynamic retrieval requirements of a user, a retrieval module is timely updated through interaction of the user and a search engine. The system comprises a data input sub system, a parameter training and predicating sub system, a retrieval performing sub system and a data output sub system, wherein the data input sub system is used for combining historical inquiry information and historical click information to form a characteristic matrix according to the current inquiry information, and acquiring a training parameter predicating module according to the characteristic matrix; the parameter training and predicating sub system is used for training and applying the parameter predicating module to acquire the predicated parameters according to the characteristic matrix; the retrieval performing sub system is used for predicating the parameters to organize the current inquiry and the historical inquiry, and combining the user module and the inquiry module to form a personalized inquiry module; and the data output sub system is used for searching a document matched with the personalized inquiry from the document to be retrieved as a primary retrieved result, and sequencing the primary retrieved result according to the correlation to obtain the final retrieved result for outputting.

Description

technical field [0001] The invention relates to computer information retrieval technology. Background technique [0002] The vastness of network information and the rapid development of related technologies make people use search engines more and more frequently. According to the statistics of China Internet Network Information Center (CNNIC), search engine (search engine) has become the most common tool to assist people in retrieving Web information. [0003] In recent years, in order to improve the accuracy of information retrieval, facilitate users to search, and improve users' search experience, many excellent information retrieval models have emerged in the field of information retrieval and have achieved good results. One of the main improvements is to establish a user interest model, which aims to ensure the relevance of documents and user interests while ensuring the content relevance of queries and documents. User interests are divided into long-term interests and...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F17/30
Inventor 杨沐昀王晓春李生齐浩亮赵铁军
Owner 哈尔滨工业大学高新技术开发总公司
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