适应用户兴趣变化的协同过滤推荐算法

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 .

A Collaborative Filtering Recommendation Algorithm Incorporated with User Interest Change

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.