Collaborative filtering method based on integration of fuzzy weight similarity measurement and clustering

A similarity measurement and collaborative filtering technology, applied in the field of recommender systems

Active Publication Date: 2014-12-24
XIDIAN UNIV +1
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AI Technical Summary

Problems solved by technology

[0005] The purpose of the embodiments of the present invention is to provide a method that combines fuzzy weight similarity measurement and clustering collaborative filtering, aiming to solve the problems of improving recommendation accuracy, data sparsity and "cold start" in collaborative filtering algorithms

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  • Collaborative filtering method based on integration of fuzzy weight similarity measurement and clustering
  • Collaborative filtering method based on integration of fuzzy weight similarity measurement and clustering
  • Collaborative filtering method based on integration of fuzzy weight similarity measurement and clustering

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Embodiment 1

[0098] refer to figure 1 , the concrete steps of the present invention are as follows:

[0099] Step 1, process the user-item rating matrix R in the training set m×n , remove users with less than 20 ratings and items that have not been rated by any user, and the corresponding users and ratings in the test set are also removed; determine the target user U i , Item I to be graded c , the nearest neighbor query number knear and the classification number kcluster;

[0100] Step 2, according to the processed scoring matrix R m×n , use fcos, fcor, fadj to calculate three different user similarity matrices FCOS, FCOR, FADJ respectively, and know the similarity between any two users from the similarity matrix;

[0101] Step 3, based on the similarity obtained in step 2, classify all users according to the k-means algorithm and the classification number kcluster;

[0102] Step 4, select user U i The class index where it is located; take the class index and the target project I c...

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Abstract

The invention discloses a collaborative filtering method based on integration of fuzzy weight similarity measurement and clustering. According to a user-item scoring matrix R<m x n>, three different similarity matrixes FCOS, FCOR and FADJ of users are respectively calculated by using fcos, fcor and fadj, and then according to a k-means algorithm and a cluster number kcluster, all users are clustered. A nearest neighbor set s (Ui) of users is determined and then scores are calculated and predicted by using r<i,c>; according to the above-mentioned strategy, the steps are repeated till scores of all user are predicated. By adopting the fuzzy similarity clustering IBCF\UBCF of the invention, the searching accuracy of the neighbor set s (Ui) is obviously improved; by fuzzifying score values and score deviations, the evaluation is closer to the real evaluation of the users to items; by adding fuzzy weight wc during similarity calculation, the similarity between the users tends to be more accurate and thus the performance of a recommender system is improved.

Description

technical field [0001] The invention belongs to the technical field of recommendation systems, in particular to a method combining fuzzy weight similarity measurement and clustering collaborative filtering. Background technique [0002] With the rapid development and popularization of the Internet and information technology, people's dependence on information is increasing day by day. The extensive use of information technology has improved the efficiency of information production, processing and dissemination. As the basic platform of the information age, the Internet carries a large amount of information resources. In the face of massive information resources, users cannot filter out the information that is useful to them, which is the problem of information overload. In order to solve the problem of information overload, recommendation system came into being. Compared with the traditional information filtering technology search engine, the recommendation system does no...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F17/30
CPCG06F16/2237G06F16/2453G06F16/24556G06F16/2462G06F16/2468G06F16/285G06F16/951G06F16/9535
Inventor 齐小刚张雅科郑耿忠刘立芳马军艳李强杨国平冯海林
Owner XIDIAN UNIV
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