ISSN 1000-1239 CN 11-1777/TP

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适应用户兴趣变化的协同过滤推荐算法

邢春晓1 高凤荣1 战思南2 周立柱2   

  1. 1(清华大学信息技术研究院Web与软件技术研究中心 北京 100084) 2(清华大学计算机科学与技术系软件研究所 北京 100084) (xingcx@tsinghua.edu.cn)
  • 出版日期: 2007-02-15

A Collaborative Filtering Recommendation Algorithm Incorporated with User Interest Change

Xing Chunxiao1, Gao Fengrong1, Zhan Sinan2, and Zhou Lizhu2   

  1. 1(Web and Software Technology Research and Development Center, Research Institute of Information Technology, Tsinghua University, Beijing 100084) 2(Institute of Software, Department of Computer Science and Technology, Tsinghua University, Beijing 100084)
  • Online: 2007-02-15

摘要: 协同过滤算法是至今为止最成功的个性化推荐技术之一,被应用到很多领域中.但传统协同过滤算法不能及时反映用户的兴趣变化.针对这个问题,提出两种改进度量:基于时间的数据权重和基于资源相似度的数据权重,在此基础上将它们有机结合,并将这两种权重引入基于资源的协同过滤算法的生成推荐过程中.实验表明,改进后的算法比传统协同过滤算法在推荐准确度上有明显提高.

关键词: 协同过滤, 个性化推荐, 基于时间的数据权重, 基于资源相似度的数据权重

Abstract: 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.

Key words: collaborative filtering, personalized recommendation, time-based data weight, item similarity-based data weight