一种相似度改进的用户聚类协同过滤推荐算法

Collaborative Filtering Recommendation Algorithm by Optimizing Similarity and Clustering Users

协同过滤算法是推荐系统中使用最普遍的个性化推荐技术.该算法基于用户评分相似度,易于实现,但是仍存在不少问题—用户评分矩阵稀疏性问题、推荐准确性问题和推荐时间效率问题.本文针对传统协同过滤算法的诸多问题,引入聚类技术,提出相似可信度、用户对项目类别喜爱度、用户对项目类别关注度三个概念,并以此来优化相似度计算,使得相似度计算更具有实际意义和准确性.然后提出基于相似度的聚类方法,将用户聚类,推荐是仅考虑同一类别中用户.实验表明,本文提出的优化方法能显著提高推荐效率.

Collaborative filtering is one of the most popular personalized recommendation methods in recommendation systems. Although the algorithm is easy to implement, it still has many problems-- data sparseness, accuracy and efficiency. For solving this, weoptimizethe original algorithm by using clustering method. In this paper, we propose similarity-credibility, cluster-favor and clusterattention to optimize the user similarity. Thus ,it makes the similarity calculation more meaningful and accurate. Then we cluster the users based on the optimized similarities. We can just consider the users in the same cluster when recommending. The experimental results show that the optimized algorithm can efficiently improve the accurate.