Authors:
Benjamin Gras
;
Armelle Brun
and
Anne Boyer
Affiliation:
Université de Lorraine, France
Keyword(s):
Atypical Preferences, Atypical Users, Recommender Systems, Collaborative Filtering, Accuracy of Recommendations.
Related
Ontology
Subjects/Areas/Topics:
Enterprise Information Systems
;
Recommendation Systems
;
Software Agents and Internet Computing
;
User Modeling
;
Web Information Systems and Technologies
;
Web Interfaces and Applications
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 prefere
nces). 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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