An algorithmic framework for performing collaborative filtering
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
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PPSN VI Proceedings of the 6th International Conference on Parallel Problem Solving from Nature
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Collaborative filtering with ordinal scale-based implicit ratings for mobile music recommendations
Information Sciences: an International Journal
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Knowledge-Based Systems
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Information Sciences: an International Journal
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ICTAI '10 Proceedings of the 2010 22nd IEEE International Conference on Tools with Artificial Intelligence - Volume 02
Information Sciences: an International Journal
Collaborative filtering based on significances
Information Sciences: an International Journal
A collaborative filtering similarity measure based on singularities
Information Processing and Management: an International Journal
Alleviating the sparsity problem of collaborative filtering using trust inferences
iTrust'05 Proceedings of the Third international conference on Trust Management
A framework for collaborative filtering recommender systems
Expert Systems with Applications: An International Journal
Knowledge-Based Systems
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Recommender systems are a type of solution to the information overload problem suffered by users of websites that allow the rating of certain items. The collaborative filtering recommender system is considered to be the most successful approach, as it makes its recommendations based on ratings provided by users who are similar to the active user. Nevertheless, the traditional collaborative filtering method can select insufficiently representative users as neighbours of the active user. This means that recommendations made a posteriori are not sufficiently precise. The method proposed in this paper uses Pareto dominance to perform a pre-filtering process eliminating less representative users from the k-neighbour selection process while retaining the most promising ones. The results from experiments performed on the Movielens and Netflix websites show significant improvements in all tested quality measures when the proposed method is applied.