A Collaborative Filtering Framework Based on Fuzzy Case-Based Reasoning

Abstract

Personalized recommendation systems have gained an increasing importance with the rapid development of Internet technologies. Collaborative filtering (CF) is the most promising technique in recommender systems, providing personalized recommendations to users based on their previously expressed preferences and those of other similar users. However, data sparsity and prediction accuracy are still major concerns related to CF techniques. Generally, the user-item matrix is quite sparse, which directly leads to the poor quality of predictions. In order to handle these problems, this paper proposes a novel approach to CF employing fuzzy case-based reasoning (FCBR), called CF-FCBR technique. Using fuzzy set theory for the computation of similarity between users and items, the proposed approach is twofold: offline and online. The offline processing is used to predict the missing values of user-item matrix and the online processing is employed for the process of recommendations generation. Our proposed approach helps in alleviating sparsity problem thereby improving recommendation accuracy. The experimental results clearly reveal that the proposed scheme, CF-FCBR is better than other traditional methods.