Identifying Grey Sheep Users in Collaborative Filtering: a Distribution-Based Technique
Identifieur interne : 000005 ( Main/Exploration ); précédent : 000004; suivant : 000006Identifying Grey Sheep Users in Collaborative Filtering: a Distribution-Based Technique
Auteurs : Benjamin Gras [France] ; Armelle Brun [France] ; Boyer Anne [France]Source :
English descriptors
Abstract
The collaborative filtering (CF) approach in recommender systems assumesthat 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 ofusers who have unusual preferences, such users are not compliant with the CFunderlying 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 identificationtechnique, that borrows from outlier detection and from informationretrieval, while taking into account the specificities of preferencedata on which CF relies: extreme sparsity, imprecision and users' bias. The experimental evaluation conducted on a state-of-the-art datasetshows that this new distribution-based technique outperforms state-of-the-art grey sheep users identification techniques.
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BP 10578153 Le Chesnay Cedex</addrLine><country key="FR"></country></address><ref type="url">http://www.inria.fr/en/</ref></desc></org></tutelle><tutelle active="#struct-413289" type="indirect"><org type="institution" xml:id="struct-413289" status="VALID"><idno type="IdRef">157040569</idno><idno type="IdUnivLorraine">[UL]100--</idno><orgName>Université de Lorraine</orgName><orgName type="acronym">UL</orgName><date type="start">2012-01-01</date><desc><address><addrLine>34 cours Léopold - CS 25233 - 54052 Nancy cedex</addrLine><country key="FR"></country></address><ref type="url">http://www.univ-lorraine.fr/</ref></desc></org></tutelle><tutelle name="UMR7503" active="#struct-441569" type="indirect"><org type="institution" xml:id="struct-441569" status="VALID"><idno type="ISNI">0000000122597504</idno><idno type="IdRef">02636817X</idno><orgName>Centre National de la Recherche Scientifique</orgName><orgName type="acronym">CNRS</orgName><date type="start">1939-10-19</date><desc><address><country key="FR"></country></address><ref type="url">http://www.cnrs.fr/</ref></desc></org></tutelle></tutelles></hal:affiliation><country>France</country><placeName><settlement type="city">Nancy</settlement><settlement type="city">Metz</settlement><region type="region" nuts="2">Grand Est</region><region type="old region" nuts="2">Lorraine (région)</region></placeName><orgName type="university">Université de Lorraine</orgName></affiliation></author></analytic></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="mix" xml:lang="en"><term> Collaborative filtering</term><term> Grey sheep users</term><term> Outlier detection</term><term>Recommender systems</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">The collaborative filtering (CF) approach in recommender systems assumesthat 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 ofusers who have unusual preferences, such users are not compliant with the CFunderlying 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 identificationtechnique, that borrows from outlier detection and from informationretrieval, while taking into account the specificities of preferencedata on which CF relies: extreme sparsity, imprecision and users' bias. The experimental evaluation conducted on a state-of-the-art datasetshows that this new distribution-based technique outperforms state-of-the-art grey sheep users identification techniques.</div></front></TEI><affiliations><list><country><li>France</li></country><region><li>Grand Est</li><li>Lorraine (région)</li></region><settlement><li>Metz</li><li>Nancy</li></settlement><orgName><li>Université de Lorraine</li></orgName></list><tree><country name="France"><region name="Grand Est"><name sortKey="Gras, Benjamin" sort="Gras, Benjamin" uniqKey="Gras B" first="Benjamin" last="Gras">Benjamin Gras</name></region><name sortKey="Anne, Boyer" sort="Anne, Boyer" uniqKey="Anne B" first="Boyer" last="Anne">Boyer Anne</name><name sortKey="Brun, Armelle" sort="Brun, Armelle" uniqKey="Brun A" first="Armelle" last="Brun">Armelle Brun</name></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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