基于用户聚类的协同过滤推荐方法

Collaborative Filtering Recommendation Method Based on Clustering of Users

为了提高电子商务网站的个性化服务效率,给出了一种改进的用户聚类的协同过滤推荐方法,该算法利用用户对项目的关注的相似性来修正原始相似性计算,综合考虑用户对项目的关注和用户评价对推荐的影响。实验表明,该基于用户聚类的协同过滤推荐算法不仅减少了用户在寻找最近邻居的搜索强度,加快了推荐生成速度,而且增强了推荐算法的实时性,提高了推荐质量。

In order to raise service efficiency of the recommendation system, an improved collaborative filtering recommendation method based on clustering of users is proposed. This new method revises the original similarity using users＇ interest in item, takes synthetically into account the influence of users＇ interest in item and users rating. The experimental results show that the presented method not only reduces the search space for nearest neighbors but also improves the performance of CF systems in recommendation quality and efficiency.