Abstract
The social approach in recommender systems relies on the hypothesis that preferences are coherent between users. To recommend a user u some resources, this approach exploits the preferences of other users who have preferences similar to those of u. Although this approach has shown to produce on average high quality recommendations, which makes it the most commonly used approach, some users are not satisfied: they get low quality recommendations. Being able to anticipate if a recommender will provide a given user with inaccurate recommendations, would be a major advantage. Nevertheless, little attention has been paid in the literature to studying this particular point. In this work, we assume that some of the users who are not satisfied do not respect the assumption made by the social approach of recommendation: their preferences are not coherent with those of others; we consider they have atypical preferences. We propose measures to identify these users, upstream of the recommendation process. These measures only exploit the users profile. The experiments conducted on a state of the art corpus and three social recommendation techniques show that the proposed measures allow to identify reliably a subset of users with atypical preferences, who will actually get inaccurate recommendations with a social approach. One of these measures is the most accurate, whatever is the recommendation technique.
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Gras, B., Brun, A., Boyer, A. (2016). When Users with Preferences Different from Others Get Inaccurate Recommendations. In: Monfort, V., Krempels, KH., Majchrzak, T.A., Turk, Ž. (eds) Web Information Systems and Technologies. WEBIST 2015. Lecture Notes in Business Information Processing, vol 246. Springer, Cham. https://doi.org/10.1007/978-3-319-30996-5_10
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DOI: https://doi.org/10.1007/978-3-319-30996-5_10
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