Collaborative filtering recommendation algorithm incorporated with user interest change

被引：30

作者：

Xing, Chunxiao ^{[1
]}

Gao, Fengrong ^{[1
]}

Zhan, Sinan ^{[2
]}

Zhou, Lizhu ^{[2
]}

机构：

[1] Web and Software Technology Research and Development Center, Research Institute of Information Technology, Tsinghua University

[2] Institute of Software, Department of Computer Science and Technology, Tsinghua University

来源：

Jisuanji Yanjiu yu Fazhan/Computer Research and Development | 2007年 / 44卷 / 02期

关键词：

Collaborative filtering; Item similarity-based data weight; Personalized recommendation; Time-based data weight;

D O I：

10.1360/crad20070216

中图分类号：

学科分类号：

摘要：

Collaborative filtering is one of the most successful technologies for building recommender systems, and is extensively used in many personalized systems. However, existing collaborative filtering algorithms do not consider the change of user interests. For this reason, the systems may recommend unsatisfactory items when user's interest has changed. To solve this problem, two new data weighting methods: time-based data weight and item similarity-based data weight are proposed, to adaptively track the change of user interests. Based on the analysis, the advantages of both weighting methods are combined efficiently and applied to the recommendation generation process. Experimental results show that the proposed algorithm outperforms the traditional item-based collaborative filtering algorithm.

引用

页码：296 / 301

页数：5

共 10 条

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