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Collaborative filtering recommendation method based on typicality and trusted network

A collaborative filtering recommendation and typicality technology, applied in special data processing applications, instruments, electrical digital data processing, etc., can solve problems such as inability to recommend, less historical user rating data, and difficult similarity calculations

Inactive Publication Date: 2017-03-08
SUN YAT SEN UNIV +3
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AI Technical Summary

Problems solved by technology

The problem of data sparsity lies in the lack of historical user rating data in the system, and it is difficult to have accurate similarity calculations when calculating the similarity between users or items; the cold start problem lies in the problem that new users cannot be recommended when they do not have historical rating data; The permanent problem is that when new users or projects are added, the system needs to increase the calculation load

Method used

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  • Collaborative filtering recommendation method based on typicality and trusted network
  • Collaborative filtering recommendation method based on typicality and trusted network
  • Collaborative filtering recommendation method based on typicality and trusted network

Examples

Experimental program
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Embodiment 1

[0080] Such as figure 1 As shown, a collaborative filtering recommendation method based on typicality and trust network includes the following steps:

[0081] Step 1: Traverse all users in the current network system to obtain user and historical rating data, project and user characteristic information.

[0082] Step 2: Preprocessing data: express the content label of each item in the form of feature text vector, set the number of hidden topics K, and use the LDA topic model to cluster the items to generate item set topic distribution θ and topic-word distribution Φ , where the itemset consists of items O i A collection of represents:

[0083]

[0084] Where m represents the number of items, O represents the item, and w i,m Indicates how typical the item is in the program set.

[0085] Through topic model clustering, the word vector (cluster center feature vector) in the topic obtained is composed of item set attributes and values, expressed as follows:

[0086]

[0...

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Abstract

The present invention provides a collaborative filtering recommendation method based on typicality and a trusted network. According to the method, an original sparse scoring matrix is replaced with a dense user typicality matrix and a project typicality matrix, and a user-trusted network is also used to improve a traditional collaborative filtering recommendation algorithm. A sparseness problem referring to little user score data in the traditional collaborative filtering recommendation method is solved by using the project typicality matrix in a project set and the user typicality matrix in a certain project set that the user is interested in, the user-trusted network is also used to further improve the recommendation accuracy, and data dimensionality deduction is realized. The recommendation result can fully use the impact of a user social trust relationship on similar user interests.

Description

technical field [0001] The present invention relates to the technical field of personalized recommendation, and more specifically, to a collaborative filtering recommendation method based on typicality and trust network. Background technique [0002] In recent years, with the rapid development of the Internet and the massive growth of information data, it is difficult for Internet users to accurately and efficiently find the information they are interested in, which has become an urgent problem to be solved in the development of the Internet. According to the user's interest characteristics, combined with project characteristics, user relationships, latent factors, etc. to build solutions, personalized recommendation technology came into being. Today's Internet website applications are more and more integrated with social factors. Users' social data can easily share personal interests and wider interaction methods, such as following and commenting, forwarding and favorites, ...

Claims

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Application Information

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IPC IPC(8): G06F17/30
CPCG06F16/9535
Inventor 陈叶彤汪静印鉴
Owner SUN YAT SEN UNIV
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