User-Feature Model for Hybrid Recommender System
Identifieur interne : 000034 ( Hal/Corpus ); précédent : 000033; suivant : 000035User-Feature Model for Hybrid Recommender System
Auteurs : Sonia Ben Ticha ; Azim Roussanaly ; Anne Boyer ; Khaled BsaiesSource :
English descriptors
Abstract
Recommender systems provide relevant items to users from a large number of choices. In this work, we are interested in personalized recommender systems where user model is based on an analysis of usage. Collaborative filtering and content-based filtering are the most widely used techniques in personalized recommender systems. Each technique has its drawbacks, so hybrid solutions, combining the two techniques, have emerged to overcome their disadvantages and benefit from their strengths. In this paper, we propose a hybrid solution combining collaborative filtering and content-based filtering. With this aim, we have defined a new user model, called user- feature model, to model user preferences based on items' features and user ratings. The user-feature model is built from the user item model by using a fuzzy clustering algorithm: the Fuzzy C Mean (FCM) algorithm. Then, we used the user-feature model in a user-based collaborative filtering algorithm to calculate the similarity between users. Applying our approach to the MoviesLens dataset, significant improvement can be noticed comparatively to the main CF algorithm, denoted as user-based collaborative filtering.
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Campus Universitaire el Manar 2092 Tunis le Belvédaire Tunisie</addrLine><country key="FR"></country></address></desc><listRelation><relation active="#struct-300651" type="direct"></relation></listRelation><tutelles><tutelle active="#struct-300651" type="direct"><org type="institution" xml:id="struct-300651" status="VALID"><orgName>Faculté des Sciences de Tunis [Tunis]</orgName><desc><address><country key="TN"></country></address><ref type="url">http://www.fst.rnu.tn/fr/</ref></desc></org></tutelle></tutelles></hal:affiliation></affiliation></author></analytic></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="mix" xml:lang="en"><term>Collaborative filtering</term><term>Content-based filtering</term><term>Fuzzy clustering</term><term>Hybrid recommender system</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">Recommender systems provide relevant items to users from a large number of choices. In this work, we are interested in personalized recommender systems where user model is based on an analysis of usage. Collaborative filtering and content-based filtering are the most widely used techniques in personalized recommender systems. Each technique has its drawbacks, so hybrid solutions, combining the two techniques, have emerged to overcome their disadvantages and benefit from their strengths. In this paper, we propose a hybrid solution combining collaborative filtering and content-based filtering. With this aim, we have defined a new user model, called user- feature model, to model user preferences based on items' features and user ratings. The user-feature model is built from the user item model by using a fuzzy clustering algorithm: the Fuzzy C Mean (FCM) algorithm. Then, we used the user-feature model in a user-based collaborative filtering algorithm to calculate the similarity between users. Applying our approach to the MoviesLens dataset, significant improvement can be noticed comparatively to the main CF algorithm, denoted as user-based collaborative filtering.</div></front></TEI><hal api="V3"><titleStmt><title xml:lang="da">User-Feature Model for Hybrid Recommender System</title><author role="aut"><persName><forename type="first">Sonia</forename><surname>Ben Ticha</surname></persName><email></email><idno type="halauthor">595293</idno><orgName ref="#struct-367815"></orgName><affiliation ref="#struct-54311"></affiliation></author><author role="aut"><persName><forename type="first">Azim</forename><surname>Roussanaly</surname></persName><email>Azim.Roussanaly@loria.fr</email><idno type="halauthor">59102</idno><affiliation ref="#struct-54311"></affiliation></author><author role="aut"><persName><forename type="first">Anne</forename><surname>Boyer</surname></persName><email>anne.boyer@loria.fr</email><idno type="halauthor">357417</idno><orgName ref="#struct-206040"></orgName><affiliation ref="#struct-54311"></affiliation></author><author role="aut"><persName><forename type="first">Khaled</forename><surname>Bsaies</surname></persName><email></email><idno type="halauthor">595294</idno><orgName ref="#struct-300651"></orgName><affiliation ref="#struct-60240"></affiliation></author><editor role="depositor"><persName><forename>Azim</forename><surname>Roussanaly</surname></persName><email>Azim.Roussanaly@loria.fr</email></editor></titleStmt><editionStmt><edition n="v1" type="current"><date type="whenSubmitted">2011-03-28 13:22:12</date><date type="whenModified">2016-05-19 01:05:28</date><date type="whenReleased">2011-03-28 14:16:39</date><date type="whenProduced">2011-03-17</date><date type="whenEndEmbargoed">2011-03-28</date><ref type="file" target="https://hal.inria.fr/inria-00580523/document"><date notBefore="2011-03-28"></date></ref><ref type="file" subtype="author" n="1" target="https://hal.inria.fr/inria-00580523/file/siie2011_submission_68.pdf"><date notBefore="2011-03-28"></date></ref></edition><respStmt><resp>contributor</resp><name key="103167"><persName><forename>Azim</forename><surname>Roussanaly</surname></persName><email>Azim.Roussanaly@loria.fr</email></name></respStmt></editionStmt><publicationStmt><distributor>CCSD</distributor><idno type="halId">inria-00580523</idno><idno type="halUri">https://hal.inria.fr/inria-00580523</idno><idno type="halBibtex">benticha:inria-00580523</idno><idno type="halRefHtml">ESCE- University la Manouba (Tunisia), Nancy University (France), IGA (Morocco), IEEE Morocco section. 4th International Conference on Information Systems and Economic Intelligence - SIIE'2011, Mar 2011, Marrakech, Morocco. IGA Maroc, 2011, nformation Systems and Economic Intelligence (SIIE'2011) : 4th International Conference. (Proceedings)</idno><idno type="halRef">ESCE- University la Manouba (Tunisia), Nancy University (France), IGA (Morocco), IEEE Morocco section. 4th International Conference on Information Systems and Economic Intelligence - SIIE'2011, Mar 2011, Marrakech, Morocco. IGA Maroc, 2011, nformation Systems and Economic Intelligence (SIIE'2011) : 4th International Conference. (Proceedings)</idno></publicationStmt><seriesStmt><idno type="stamp" n="CNRS">CNRS - Centre national de la recherche scientifique</idno><idno type="stamp" n="INRIA">INRIA - Institut National de Recherche en Informatique et en Automatique</idno><idno type="stamp" n="LABO-LORIA-SET" p="LORIA">LABO-LORIA-SET</idno><idno type="stamp" n="LORIA2">Publications du LORIA</idno><idno type="stamp" n="LORIA-CSAI" p="LORIA">Systèmes complexes et intelligence artificielle</idno><idno type="stamp" n="LORIA">LORIA - Laboratoire Lorrain de Recherche en Informatique et ses Applications</idno><idno type="stamp" n="UNIV-LORRAINE">Université de Lorraine</idno><idno type="stamp" n="INPL">Institut National Polytechnique de Lorraine</idno></seriesStmt><notesStmt><note type="audience" n="2">International</note><note type="invited" n="0">No</note><note type="popular" n="0">No</note><note type="peer" n="1">Yes</note><note type="proceedings" n="1">Yes</note></notesStmt><sourceDesc><biblStruct><analytic><title xml:lang="da">User-Feature Model for Hybrid Recommender System</title><author role="aut"><persName><forename type="first">Sonia</forename><surname>Ben Ticha</surname></persName><idno type="halAuthorId">595293</idno><orgName ref="#struct-367815"></orgName><affiliation ref="#struct-54311"></affiliation></author><author role="aut"><persName><forename type="first">Azim</forename><surname>Roussanaly</surname></persName><email>Azim.Roussanaly@loria.fr</email><idno type="halAuthorId">59102</idno><affiliation ref="#struct-54311"></affiliation></author><author role="aut"><persName><forename type="first">Anne</forename><surname>Boyer</surname></persName><email>anne.boyer@loria.fr</email><idno type="halAuthorId">357417</idno><orgName ref="#struct-206040"></orgName><affiliation ref="#struct-54311"></affiliation></author><author role="aut"><persName><forename type="first">Khaled</forename><surname>Bsaies</surname></persName><idno type="halAuthorId">595294</idno><orgName ref="#struct-300651"></orgName><affiliation ref="#struct-60240"></affiliation></author></analytic><monogr><meeting><title>4th International Conference on Information Systems and Economic Intelligence - SIIE'2011</title><date type="start">2011-03-17</date><date type="end">2011-03-19</date><settlement>Marrakech</settlement><country key="MA">Morocco</country></meeting><respStmt><resp>conferenceOrganizer</resp><name>IGA, Casablanca Maroc</name></respStmt><editor>ESCE- University la Manouba (Tunisia), Nancy University (France), IGA (Morocco), IEEE Morocco section</editor><imprint><publisher>IGA Maroc</publisher><biblScope unit="serie">nformation Systems and Economic Intelligence (SIIE'2011) : 4th International Conference. (Proceedings)</biblScope><date type="datePub">2011-02-17</date></imprint></monogr></biblStruct></sourceDesc><profileDesc><langUsage><language ident="en">English</language></langUsage><textClass><keywords scheme="author"><term xml:lang="en">Collaborative filtering</term><term xml:lang="en">Content-based filtering</term><term xml:lang="en">Fuzzy clustering</term><term xml:lang="en">Hybrid recommender system</term></keywords><classCode scheme="acm" n="H.3.1"></classCode><classCode scheme="halDomain" n="info.info-wb">Computer Science [cs]/Web</classCode><classCode scheme="halTypology" n="COMM">Conference papers</classCode></textClass><abstract xml:lang="en">Recommender systems provide relevant items to users from a large number of choices. In this work, we are interested in personalized recommender systems where user model is based on an analysis of usage. Collaborative filtering and content-based filtering are the most widely used techniques in personalized recommender systems. Each technique has its drawbacks, so hybrid solutions, combining the two techniques, have emerged to overcome their disadvantages and benefit from their strengths. In this paper, we propose a hybrid solution combining collaborative filtering and content-based filtering. With this aim, we have defined a new user model, called user- feature model, to model user preferences based on items' features and user ratings. The user-feature model is built from the user item model by using a fuzzy clustering algorithm: the Fuzzy C Mean (FCM) algorithm. Then, we used the user-feature model in a user-based collaborative filtering algorithm to calculate the similarity between users. Applying our approach to the MoviesLens dataset, significant improvement can be noticed comparatively to the main CF algorithm, denoted as user-based collaborative filtering.</abstract></profileDesc></hal></record>Pour manipuler ce document sous Unix (Dilib)
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