A Compact User Model for Hybrid Movie Recommender System

Abstract

Collaborative filtering (CF), the most successful information filtering technique for recommender systems, is either memory-based or model-based. While the former is more accurate, its scalability compared to model-based is poor. Moreover, the similarity functions used by most recommender systems are compensatory and allow very high (pros) and very low (cons) scores to compensate each other. This paper presents a hybrid movie recommender system that retains memory-based CF accuracy, model-based CF scalability, and alleviates the compensation problem of similarity functions. The proposed recommender system relies on a compact user model and fuzzy concordance/discordance principle. The user model speeds up the online process of generating a set of like-minded users within which a memory-based CF is carried out. The inter users comparison is done by using fuzzy concordance/discordance principle to alleviate the similarity compensation problem. The pros and cons between users are measured separately and then the overall statement about them is obtained by balancing the pros and cons within the set of criteria. Besides our approach is fast and compact, computational results reveal that it outperforms the classical one.