Improving accuracy of recommendation system by means of Item-based Fuzzy Clustering Collaborative Filtering
Cosimo Birtolo, Davide Ronca, Roberto Armenise · 2011
Predicting user preferences is a challenging task. Different approaches for recommending products to the users are proposed in literature and collaborative filtering has been proved to be one of the most successful techniques. Some issues related to the quality of recommendation and to computational aspects still arise (e.g., scalability and cold-start recommendations). In this paper, we propose an Item-based Fuzzy Clustering Collaborative Filtering (IFCCF) in order to ensure the benefits of a model-based technique improving the quality of suggestions. Experimentation led by predicting ratings of MovieLens and Jester users makes this promising and worth to be further investigated in a cross-domain dataset.