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Identifying Grey Sheep Users in Collaborative Filtering: a Distribution-Based Technique

Benjamin Gras 1 Armelle Brun 1 Boyer Anne 1
1 KIWI - Knowledge Information and Web Intelligence
LORIA - AIS - Department of Complex Systems, Artificial Intelligence & Robotics
Abstract : The collaborative filtering (CF) approach in recommender systems assumes that users' preferences are consistent among users. Although accurate, this approach fails on some users. We presume that some of these users belong to a small community of users who have unusual preferences, such users are not compliant with the CF underlying assumption. They are {\it grey sheep users}. This paper aims at accurately identifying grey sheep users. We introduce a new distribution-based grey sheep users identification technique, that borrows from outlier detection and from information retrieval, while taking into account the specificities of preference data on which CF relies: extreme sparsity, imprecision and users' bias. The experimental evaluation conducted on a state-of-the-art dataset shows that this new distribution-based technique outperforms state-of-the-art grey sheep users identification techniques.
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https://hal.inria.fr/hal-01303284
Contributor : Armelle Brun <>
Submitted on : Sunday, April 17, 2016 - 5:48:44 PM
Last modification on : Thursday, December 12, 2019 - 1:00:42 PM

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Benjamin Gras, Armelle Brun, Boyer Anne. Identifying Grey Sheep Users in Collaborative Filtering: a Distribution-Based Technique. ACM UMAP, Jul 2016, Halifax, Canada. pp.17-26, ⟨10.1145/2930238.2930242⟩. ⟨hal-01303284⟩

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