摘要： 随着用户和项目数量的增长，用户-项目评分矩阵变得极其稀疏，导致基于相似度计算的推荐算法精度降低。为此，提出一种基于加权Jaccard系数的综合项目相似度度量方法，使用项目综合相似度对评分矩阵进行预填充。实验结果表明，在用户-项目评分矩阵极其稀疏的情况下，该算法能产生比传统算法更精确的推荐结果。

关键词: 推荐算法, 协同过滤, 相似度, 信息熵, 加权Jaccard系数

Abstract: When the magnitudes of users and commodities grow rapidly, the rating matrix becomes extremely sparse. In the condition, algorithms based on traditional similarity computing have poor performance. In order to overcome this problem, this paper proposes a comprehensive item similarity measurement algorithm based on weighted Jaccard index, and prefills the rating matrix by the comprehensive item similarity. Experimental results show that the algorithm is more accurate compared with traditional algorithms.

Key words: recommendation algorithm, collaborative filtering, similarity, information entropy, weighted Jaccard coefficient

中图分类号: 

• TP312