Multi-attribute Collaborative Filtering Recommendation
نویسندگان
چکیده
Currently researchers in the field of personalized recommendations bear little consideration on users’ interest differences in resource attributes although resource attribute is usually one of the most important factors in determining user preferences. To solve this problem, the paper builds an evaluation model of user interest based on resource multi-attributes, proposes a modified Pearson-Compatibility multiattribute 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 algorithm has a high accuracy on predicting target users’ attribute preferences and has a strong anti-interference ability on deviation and incomplete values.
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