Abstract
The recommendation framework based on precedence mining as outlined in [3] is limited to personal recommendation and cannot be trivially extended for group recommendation scenario. In this paper, we extend the precedence mining model for group recommendation by proposing a novel way of defining a virtual user by taking transitive precedence relation into account. We obtained experimental results for different combinations of parameter settings and for different group-sizes on MovieLens data-set based on our virtual-user model. We show that our framework has better performance in terms of precision and recall when compared with other methods.
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© 2013 Springer International Publishing Switzerland
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Kagita, V.R., Pujari, A.K., Padmanabhan, V. (2013). Group Recommender Systems: A Virtual User Approach Based on Precedence Mining. In: Cranefield, S., Nayak, A. (eds) AI 2013: Advances in Artificial Intelligence. AI 2013. Lecture Notes in Computer Science(), vol 8272. Springer, Cham. https://doi.org/10.1007/978-3-319-03680-9_43
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DOI: https://doi.org/10.1007/978-3-319-03680-9_43
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-03679-3
Online ISBN: 978-3-319-03680-9
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