用户间多相似度协同过滤推荐算法

传统的User-based协同过滤推荐算法仅采用了单一的评分相似度来度量用户之间对任何项目喜好的相似程度。然而根据日常经验,人们对不同类型事物的喜好程度往往是不同的,单一的评分相似度显然无法准确描述这种不同。针对上述问题,提出了一种基于用户间多相似度的协同过滤推荐算法,即基于用户间对不同项目类型的多个评分相似度来计算用户对未评分项目的预测评分。实验结果表明,该算法可以有效地提高预测评分的准确性及推荐质量。

Collaborative Filtering Recommendation Algorithm Based on User's Multi-similarity

Conventional user-based collaborative filtering algorithm measures the similarity of two user's favor of any types of items through the single rating similarity. However, daily experience tells us that people usually have different degree on their favor of different types of objects,and obviously the single rating similarity cannot accurately describethis difference. Aiming at this problem, we deeply analyzed the characteristic of user-based collaborative filtering recommendation algorithm, and proposed a collaborative filtering recommendation algorithm based on user's multi-similarity,which describes different similarity of two user's favor of different types of items through the computation of their multiple independent rating similarity for different types of items. The experimental results show that the proposed algorithm, which computes predicted ratings of unrated items on the basis of user's multi-similarity, can effectively improve the accuracy of predicted ratings and enhance the quality of recommendation.