An algorithmic framework for performing collaborative filtering
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An Improvement to Collaborative Filtering for Recommender Systems
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Expert Systems with Applications: An International Journal
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Collaborative filtering adapted to recommender systems of e-learning
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Computers and Operations Research
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Information Processing and Management: an International Journal
A framework for collaborative filtering recommender systems
Expert Systems with Applications: An International Journal
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Knowledge-Based Systems
A balanced memory-based collaborative filtering similarity measure
International Journal of Intelligent Systems
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Information Processing and Management: an International Journal
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Hybrid recommendation approaches for multi-criteria collaborative filtering
Expert Systems with Applications: An International Journal
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In this paper we present a collaborative filtering method which opens up the possibilities of traditional collaborative filtering in two aspects: (1) it enables joint recommendations to groups of users and (2) it enables the recommendations to be restricted to items similar to a set of reference items. By way of example, a group of four friends could request joint recommendations of films similar to ''Avatar'' or ''Titanic''. In the paper, using experiments, we show that the traditional approach of collaborative filtering does not satisfactorily resolve the new possibilities contemplated; we also provide a detailed formulation of the method proposed and an extensive set of experiments and comparative results which show the superiority of designed collaborative filtering compared to traditional collaborative filtering in: (a) number of recommendations obtained, (b) quality of the predictions, (c) quality of the recommendations. The experiments have been carried out on the databases Movielens and Netflix.