关键词: 协同过滤, 信任计算模型, 用户相似度, 药品推荐

Abstract: Aiming at the problem of lower recommendation precision of collaborative filtering, medicine recommendation algorithm based on user similarity and trust is proposed. The method clusters drugs into several groups by using DBSCAN algorithm offline to reduce the time complexity. For the sake of computing the user similarity more precisely, co-rated drugs threshold is introduced to build similar neighbor set of target user, at the same time similarity threshold is introduced to restrict the selection of similar neighbors, which overcomes the defects of KNN algorithm. Then the trust computing model is designed according to recommendation credibility and score reliability. The trustworthy neighbor set of target user is selected in accordance with the degree of trust between users. Finally, drugs are recommended to target user through twice neighbor selection strategy. Experimental results show that compared with the existing algorithms, the proposed algorithm has better performance in mean absolute error, precision and recall ratio, which improves the recommendation precision.

Key words: collaborative filtering, trust computing model, user similarity, medicine recommendation