A Cold-start Recommendation Algorithm Based on New Users Implicit Information and Multi-Attribute Rating Matrix
Traditional collaborative filtering recommendation algorithms face the cold-start problem. A collaborative filtering recommendation algorithm based on the implicit information of the new users and multi-attribute rating matrix is proposed to solve the problem. The implicit information of the new users is collected as the first-hand interest information. It is combined with other rating information to create a User-Item Rating Matrix (UIRM). Singular Value Decomposition is used to reduce the dimensionality of the UIRM, resulting in the initial neighbor set for target users and a new user-item rating matrix. The user ratings are mapped to the relevant item attributes and the user attributes respectively to generate a User-Item Attribute Rating Matrix and a User Attribute- Item Attribute Rating Matrix (UAIARM). The attributes of new items and UAIARM are matched to find the N users with the highest match degrees as the target of the new items. The attributes of the new users are matched with UAIARM to find the N items with the highest match degrees as the recommended items. Experiment results validate the feasibility of the algorithm.
collaborative filtering implicit information attribute rating matrix cold-start recommendation algorithm
YIN Hang CHANG Guiran WANG Xingwei
School of Information Science and Engineering Northeastern University Shenyang, China
国际会议
2009 Ninth International Conference on Hybrid Intelligent Systems(第九届混合智能系统国际会议 HIS 2009)
沈阳
英文
1-6
2009-08-12（万方平台首次上网日期，不代表论文的发表时间）