Abstract
Recommender Systems seek to furnish personalized suggestions automatically based on user preferences. These preferences are usually expressed as a set of items either directly or indirectly given by the user (e.g., the set of products the user bought in a virtual store). In order to suggest new items, Recommender Systems generally use one of the following approaches: Content Based Filtering, Collaborative Filtering or hybrid filtering methods. In this paper we propose a strategy to improve the quality of recommendation in the first user contact with the system. Our approach includes a suitable plan to acquiring a user profile and a hybrid filtering method based on Modal Symbolic Data. Our proposed technique outperforms the Modal Symbolic Content Based Filter and the standard kNN Collaborative Filter based on Pearson Correlation.
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© 2004 Springer-Verlag Berlin Heidelberg
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Bezerra, B., de A. T. de Carvalho, F. (2004). A Symbolic Hybrid Approach to Face the New User Problem in Recommender Systems. In: Webb, G.I., Yu, X. (eds) AI 2004: Advances in Artificial Intelligence. AI 2004. Lecture Notes in Computer Science(), vol 3339. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30549-1_94
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DOI: https://doi.org/10.1007/978-3-540-30549-1_94
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-24059-4
Online ISBN: 978-3-540-30549-1
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