Abstract
Personalized recommender systems provide relevant items to users from huge catalogue. Collaborative filtering (CF) and content-based (CB) filtering are the most widely used techniques in personalized recommender systems. CFuses only the user-rating data to make predictions, while CB filtering relies on semantic information of items for recommendation. In this paper we present a new approach taking into account the semantic information of items in a CF system. Many works have addressed this problem by proposing hybrid solutions. In this paper, we present another hybridization technique that predicts users ‘preferences for items based on their inferred preferences for semantic information. With this aim, we propose a new approach to build user semantic profile to model users’ preferences for semantic information of items. Then, we use this model in a user-based CF algorithm to calculate the similarity between users. We apply our approach to real data, the MoviesLens dataset, and compare our results to standards user-based and item-based CF algorithms.
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Ben Ticha, S., Roussanaly, A., Boyer, A., Bsaies, K. (2012). User Semantic Preferences for Collaborative Recommendations. In: Huemer, C., Lops, P. (eds) E-Commerce and Web Technologies. EC-Web 2012. Lecture Notes in Business Information Processing, vol 123. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32273-0_18
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DOI: https://doi.org/10.1007/978-3-642-32273-0_18
Publisher Name: Springer, Berlin, Heidelberg
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