Abstract
Recommender systems aim to furnish automatically personalized suggestions based on user preferences. These systems use information filtering (IF) techniques to recommend new items by comparing them with a user profile. This paper presents an approach where each user profile is modelled by a set of modal symbolic descriptions, which summarize the information given by the set of items already evaluated by the user. The comparison between a new item and a user profile is accomplished by a new suitable dissimilarity function which takes into account differences in content and position. This new approach is evaluated in comparison with the kNN method, which is an IF technique often used in this kind of system.
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© 2003 Springer-Verlag Berlin Heidelberg
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de A. T. de Carvalho, F., Bezerra, B.L.D. (2003). Information Filtering Based on Modal Symbolic Objects. In: Schader, M., Gaul, W., Vichi, M. (eds) Between Data Science and Applied Data Analysis. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-18991-3_45
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DOI: https://doi.org/10.1007/978-3-642-18991-3_45
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-40354-8
Online ISBN: 978-3-642-18991-3
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