Recommender Systems based on Multi- Attribute Decision Making

摘要

User based collaborative filtering systems suggest interesting items to a user relying on similar-minded people called neighbors. The selection and weighting of these neighbors characteristics the different recommendation approaches. While standard strategies perform a neighbor selection based on user similarities. The paper built 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. Here this study addresses the issues on preference differences of similar users.