关键词: 推荐系统, 协同过滤, 相似性度量, 评分偏好, 稀疏度

Abstract: Traditional collaborative filtering recommendation algorithm has some problems, such as the sparsity of rating data, the lack of user rating preference, and the limitation of traditional similarity measurement. In this paper, a twostage recommendation model combining item prediction score and user preference score is proposed. In the first stage, the itembased prediction score is used to complete the score matrix, and the time weight factor is used to improve the item similarity; In the second stage, the complete scoring matrix is transformed into a user scoring preference matrix for scoring categories by using the scoring preference model, then the preference score is calculated by using the userbased collaborative filtering algorithm through the matrix, and the user common rating score weight is used to improve the user similarity. Finally, the itembased prediction score and the userbased preference score are used as the comprehensive prediction score of the target user. Experimental results show that the proposed algorithm outperforms the traditional collaborative filtering algorithm in terms of accuracy and recall rate under different number of neighbor users and different lengths of recommendation list. Moreover, for different sparsity data sets, the MAE increment value of the proposed algorithm is reduced by 8%-24.6%, with higher recommendation precision and accuracy.

Key words: recommendation system, collaborative filtering, similarity measurement, rating preference, sparsity