Hybrid fuzzy collaborative filtering: an integration of item-based and user-based clustering techniques

Clustering is one of the successful approaches of the model-based collaborative filtering techniques that deals with the problem of sparsity and provides quality recommendations. In the proposed work, fuzzy c-means clustering technique is adopted in order to produce item-based clusters as well as user-based clusters. Subsequently, collaborative filtering technique explores the item-based and user-based clusters and generates the list of item-based and user-based predictions, respectively. Further, to enhance the quality of recommendations, a novel weighted hybrid scheme is designed which integrates the user-based and item-based predictions to capture the influence of each active user towards item-based and user-based predictions. The proposed schemes are further categorised on the basis of re-clustering and without re-clustering under different similarity measures over sparse and dense datasets. The experimental results reveal that the variants of the proposed hybrid schemes consistently generate better results in comparison with the corresponding variants of proposed user-based schemes and the traditional item-based schemes.