基于用户和项目的双视角协同过滤推荐方法

doi:10.3969/j.issn.0255-8297.2017.03.006

Abstract

Abstract:

Traditional collaborative fltering (CF) recommendation approach has a serious problems such as insufcient usage of single perspective information, unsatisfactory accuracy and sensitivity to data sparsity. To solve these problems, a CF recommendation method based on double-perspective of users and items is proposed by considering information of similar users and similar items. According to the dialectic principle, fusion similarity of items is given by combination of inner-factors and outer-factors of the item. This way, the item similarity and user similarity can be effectively measured. The measurement is robust against data sparsity in the approach of CF recommendation based on double-perspective of user and item. Several experiments are carried on benchmark datasets. The results show that the proposed CF recommendation method based on double-perspective of users and items outperforms several other typical CF approaches.

Key words: collaborative fltering recommendation, double-perspective, fusion similarity of item

CLC Number:

TP181

CHENG Shu-lin, ZHANG Bo-feng, ZOU Guo-bing. Collaborative Filtering Recommendation Based on Double-Perspective of Users and Items[J]. Journal of Applied Sciences, 2017, 35(3): 326-336.

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Collaborative Filtering Recommendation Based on Double-Perspective of Users and Items

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