关键词: 协同过滤, 相似度, 历史反馈信息, 谱聚类, 召回率

Abstract: This paper focuses on the issues that the impact of all users’ historical feedback information has been taken into account when calculating the similarities between any two items, and the traditional collaborative filtering algorithms only utilize the user’s rating data to calculate the similarities. To solve the above problems, two novel algorithms are proposed. Top-N Collaborative Filtering recommendation algorithm based on user Spectral Clustering（SC-CF）：it clusters all uses into several partitions using spectral clustering, then recommends for users in each of the clusters. SC-CF+, compared to SC-CF algorithm, draws the time difference weight, users’ common rating weight and popular items weight into the similarities formula respectively. Experimental results show that the two new algorithms improve the recall of recommendation results.

Key words: collaborative filtering, similarity, historical feedback information, spectral clustering, recall