Co-Clustering based Hybrid Collaborative Filtering Model

Recommender systems are intellectual systems of information filtering that makes relevant suggestions for customers. Main propose of generating relevant information for customers is to improve their experience. A recommender system which gives fast, accurate recommendations and good experience to users is more attractive and develop more interests in users. As the strength of internet users and items are growing day by day many challenges are faced by recommender systems. One of the challenges is data sparsity. Highly sparse data gives decreased accuracy of predictions. High quality predictions are dependent upon how well the recommender system address its challenges. This research work proposes Collaborative filtering based recommender system technique. Neighbours are determined using dimensionality reduction based Spectral Co-clustering technique. The approach presented is Confidence based weighted fusion method (CBWF) merged with the rating predictions from User based CF (UbCF) and Item based (IbCF). Along with confidence a parameter σ is used. Previously σ was varied based on the datasets. In this research parameter σ is controlled and made dependent upon the correlation characteristics of individual users and items based on their state. Spectral-co-clustering overcomes the sparseness of dataset and limitation of scalability. While fusion of UbCF and IbCF in confidence based weighted sum improves the prediction accuracy of system. Finally predictive evaluation metrics are used to compare results for proposed technique with conventional techniques, one dimensional clustering, two dimensional clustering and HCF techniques to show the improvement that this research work has made.