Smoothing approach to alleviate the meager rating problem in collaborative recommender systems
Future Generation Computer Systems
Study on user preferences modelling based on web mining
International Journal of Information Technology and Management
Personal classification space-based collaborative filtering algorithms
International Journal of Computer Applications in Technology
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Collaborative filtering is an important personalized recommendation technique applied widely in E-commerce. It is not adapted to multi-interest or title recommendation for the 'general neighbourhood' problem which is analyzed in this paper. Based on it, collaborative filtering recommendation based on community is presented by introducing the concept 'community neighbourhood' in the paper. Unfortunately, it results into severer sparsity problem which makes heavy effect on its performance. In order to overcome it, an ontological A-priori score is used to infer user preference and to pre-fill null rating first. After pre-filling using the ontology method, then collaborative filtering based on community is executed based on a dense rating matrix. The experiment shows that collaborative filtering based on community makes generally better performance than traditional method when data is not very sparse, and ontology method can truly enhance collaborative filtering based on community since the sparsity is overcame.