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Collaborative filtering method for user interest harmony similarity

A user-oriented, collaborative filtering technology, applied in special data processing applications, instruments, electronic digital data processing, etc., can solve problems such as low recommendation accuracy, insufficient timeliness, and inability to respond to changes in user interests in a timely manner

Inactive Publication Date: 2019-11-01
HENAN POLYTECHNIC UNIV
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AI Technical Summary

Problems solved by technology

However, this technology ignores the problem that the user's interest is constantly changing with time, so it cannot respond to the user's interest change in time, and the timeliness is insufficient, resulting in the problem of low recommendation accuracy.

Method used

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  • Collaborative filtering method for user interest harmony similarity
  • Collaborative filtering method for user interest harmony similarity
  • Collaborative filtering method for user interest harmony similarity

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Embodiment Construction

[0014] Such as figure 1 Shown is the collaborative filtering method of user interest reconciliation similarity, including the following steps:

[0015] (1) Use the user rating data to obtain the original rating matrix R and perform probability matrix decomposition on it to obtain an approximate rating matrix ;

[0016] (2) Traverse the original scoring matrix R and the approximate scoring matrix Get Pearson similarity based on user common interest score Sim fill_t_peirson ;

[0017] (3) Traversing the original rating matrix R to get the user rating trust factor N i ;

[0018] (4) Traverse the original rating matrix R to get the user evaluation deviation trust factor D i ;

[0019] (5) Utilize the modified user interest similarity sim fill_t_pearson , user evaluation rating trust factor N i and user evaluation deviation trust factor D i , calculate the user interest harmonic similarity sim t ;

[0020] (6) Calculate the recommendation result by using the o...

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Abstract

The invention relates to a collaborative filtering method for user interest harmony similarity. The method comprises the following steps: decomposing an original scoring matrix; introducing a time-based user interest degree weight function into a Pearson similarity function; and combining with the user evaluation grade trust factor and the user evaluation deviation trust factor to obtain user interest harmony similarity, obtaining a recommendation result by utilizing the original scoring matrix and the user interest harmony similarity, and recommending items meeting requirements to the user. According to the method provided by the invention, the recommendation precision can be remarkably improved compared with a traditional probability matrix decomposition method under the condition that the user scoring data is sparse, and the interested articles can be recommended according to the user in different time periods.

Description

technical field [0001] The invention relates to the recommendation field of data mining, in particular to a collaborative filtering method oriented to user interest reconciliation similarity. Background technique [0002] As users participate in information production, the scale of network information has exploded. Massive information provides the possibility for information retrieval and at the same time leads to information overload. In order to alleviate the above contradictions and help users accurately and quickly find the information they are interested in in massive data, recommendation systems came into being. [0003] The collaborative filtering algorithm is a commonly used method in the recommendation system. In order to solve the sparsity problem in the recommendation process, the collaborative filtering recommendation technology based on probability matrix decomposition is generally used. The common idea is to first calculate the user similarity, then determine ...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F16/9535G06F16/2458
Inventor 王建芳谷振鹏苗艳玲韩鹏飞张秋玲
Owner HENAN POLYTECHNIC UNIV
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