Fuzzy logic based similarity measure for multimedia contents recommendation

Collaborative filtering is one of the mainstream approaches to provide recommendations in various online environments such as Ecommerce. Although this is a popular method for service recommendation, it still suffers from sparsity issue where only a small number of rating records are available for some new items or users in the system. Consequently, the accuracy of rate prediction is often compromised. Unlike the conventional collaborative filtering methods that directly compute the similarity between users, this paper presents a fuzzy logic based approach to refine the similarity obtained using traditional approaches like Pearson correlation, Cosine, Adjusted Cosine etc. Experiments were conducted on the two popular benchmark datasets and it shows that the proposed method obtains better prediction accuracy as compare to other traditional similarity measures.