Book recommending method based on multi-view hash

A recommendation method, multi-view technology, applied in special data processing applications, instruments, electrical digital data processing, etc., can solve the problems of data integration without considering user behavior, low efficiency, etc.

Active Publication Date: 2015-06-03
ZHEJIANG UNIV
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AI Technical Summary

Problems solved by technology

[0003] Mining the user's interest points is usually based on the user's historical behavior data. The traditional method generally uses the user's data on a certain view, without considering ...

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  • Book recommending method based on multi-view hash
  • Book recommending method based on multi-view hash
  • Book recommending method based on multi-view hash

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Embodiment Construction

[0032] The present invention will be described in further detail below in conjunction with the accompanying drawings.

[0033] Such as figure 1 Shown, a kind of book recommendation method based on multi-view hash of the present invention comprises the following steps:

[0034] (1) Filter out the user's behavior data on the two views from the log collection system, including book click data and search data;

[0035] (2) Use the user's click data and search data to construct the user feature vector of the user on the click view and search view, specifically: get all the user's book click sets B from the user click data = {b 1 ,b 2 ,...,b x}, where x is the total number of books, and then according to the user click data and B, construct the feature vector X of each user click view 1 , X 1 =[I 1 , I 2 ,...,I x ]in Using the search data of all users, first divide all the search words of the user into words, and obtain the set of search words of all users Q={q 1 ,q 2 ,....

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Abstract

The invention discloses a book recommending method based on multi-view hash, comprising the following steps of 1) screening behavior data of a user on two views from a log collection system, wherein the behavior data comprise book clicking data and search data; 2) establishing user feature vectors of the user for clicking and searching the views; 3) obtaining a user hash code, a hash function and weights of the two views through a multi-view hash algorithm by utilization of the behavior data of the two views; 4) searching a similar user for a target user by utilization of the obtained user hash code; 5) obtaining a book set clicked by the similar user, taking the book set as a recommended candidate list, calculating the book preference degree of the target user, and returning front N books with maximal preference degree of the target user. According to the book recommending method based on the multi-view hash, on one hand, the behavior data of the user on two views can be integrated into the hash code, so that the book recommendation accuracy is improved; on the other hand, the Hamming distance calculation speed of the hash code is very fast, so that the book recommendation efficiency can be improved.

Description

technical field [0001] The invention relates to book recommendation technology based on multi-view hash, in particular to a book recommendation method based on multi-view hash. Background technique [0002] With the development of information technology, the creation and sharing of content has become easier and easier, which can allow people to obtain more information and meet people's needs, but when faced with the massive information on the Internet, users cannot accurately Obtaining the information you need from it will reduce the utilization efficiency of information on the Internet, which is the so-called information overload problem. The emergence of personalized recommendation system is to solve this problem. The recommendation system will analyze the user's information needs and hobbies based on the user's information, and recommend some specific products or information to the user. When users of digital libraries face a large number of online book resources, they ...

Claims

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Application Information

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IPC IPC(8): G06F17/30
Inventor 张寅魏宝刚洪鑫
Owner ZHEJIANG UNIV
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