Identifying Users with Atypical Preferences to Anticipate Inaccurate Recommendations

Benjamin Gras 1 Armelle Brun 1 Anne Boyer 1
1 KIWI - Knowledge Information and Web Intelligence
LORIA - AIS - Department of Complex Systems, Artificial Intelligence & Robotics
Abstract : The social approach in recommender systems relies on the hypothesis that users' preferences are coherent between users. To recommend a user some items, it uses the preferences of other users, who have preferences similar to those of this user. 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. 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 a part 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; they have atypical preferences. We propose measures to identify these users, upstream of the recommendation process, based on their profile only (their preferences). The experiments conducted on a state of the art corpus show that these measures allow to identify reliably a subset of users with atypical preferences, who will get inaccurate recommendations.
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Submitted on : Monday, January 11, 2016 - 7:29:37 PM
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Benjamin Gras, Armelle Brun, Anne Boyer. Identifying Users with Atypical Preferences to Anticipate Inaccurate Recommendations. Webist 2015 - 11th International Conference on Web Information Systems and Technologies, May 2015, Lisbonne, Portugal. ⟨10.5220/0005412703810389⟩. ⟨hal-01254172⟩

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