Multi-attribute Collaborative Filtering Recommendation
Changrui Yu, Yu Luo, Kangbo Liu, Changrui Yu, Yan Luo, Shu-Uts Silc, Kecheng Liu · 2015
Currently researchers in the field of personalized recommendations bear little consideration on users' interest differences in resource attributes although re- source attribute is usually one of the most important factors in determining user pref- erences. To solve this problem, the paper builds an evaluation model of user interest based on resource multi-attributes, proposes a modified Pearson-Compatibility multi- attribute group decision-making algorithm, and introduces an algorithm to solve the recommendation problem of k-neighbor similar users. This study addresses the issues on preference differences of similar users, incomplete values, and advanced converge of the algorithm, and realizes multi-attribute collaborative filtering. The algorithm is proved to be effective by an experiment of collaborative recommendation among multi-users in a virtual environment. The experimental results show that the algo- rithm has a high accuracy on predicting target users' attribute preferences and has a strong anti-interference ability on deviation and incomplete values.