Conference Paper

A Hybrid Preference-based Recommender System Based on Fuzzy Concordance / Discordance Principle.

Source: DBLP
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    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.
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    ABSTRACT: E-commerce systems employ recommender systems to enhance the customer loyalty and hence increasing the cross-selling of products. However, choosing appropriate similarity measure is a key to the recommender system success. Based on this measure, a set of neighbors for the current active user is formed which in turn will be used later to recommend unseen items to this active user. Pearson correlation coefficient, the most popular similarity measure for memory-based collaborative recommender system (CRS), measures how much two users are correlated. However, statistic’s literature introduced many other coefficients for matching two sets (vectors) that may perform better than Pearson correlation coefficient. This paper explores Jaccard and Dice coefficients for matching users of CRS. A more general coefficient called a Power coefficient is proposed in this paper which represents a family of coefficients. Specifically, Power coefficient gives many degrees for emphasizing on the positive matches between users. However, CRS users have positive and negative matches and therefore these coefficients have to be modified to take negative matches into consideration. Consequently, they become more suitable for CRS research. Many experiments are carried out for all the proposed variants and are compared with the traditional approaches. The experimental results show that the proposed variants outperform Pearson correlation coefficient and cosine similarity measure as they are the most common approaches for memory-based CRS.
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