中文摘要：

在现代信息网络中,个性化的推荐系统已经成为用户和应用软件交互的关键部分。推荐算法是个性化推荐系统的核心,其中,协同过滤算法是至今应用最为成功的推荐算法之一。但传统的协同过滤算法没有考虑用户兴趣的多样性,对用户兴趣度量不准确,难以适用于用户多兴趣的推荐系统,提出了适应用户兴趣多样性的协同过滤算法并利用改进的模糊聚类算法搜索最近邻。最后采用实际的日志数据进行算法实验,实验结果表明该算法较其他推荐算法具有较优的执行效率和推荐精度。

英文摘要：

In the modern information network,the personalized recommendation system has become a key part of users in software application.Recommendation algorithms are the core of personalized recommendation systems.Among them,the collaborative filtering is one of the most successful recommendation algorithm in application.However,the traditional collaborative filtering algorithm does not consider user＇s multiple interest and measure user＇s interest imprecisely,and can＇t be applied to recommendation system with kinds of interests.In this paper,a new method of collaborative filtering algorithm based on users＇ interest category is proposed using improved fuzzy clustering algorithm to search the nearest neighbors.Finally,the algorithm experiment is given with actual log-data.Results show that the proposed algorithm outperforms the other recommendation ones in efficiency and recommending accuracy.