Densifying a Behavioral Recommender System by Social Networks link prediction methods
Identifieur interne : 001B51 ( Hal/Corpus ); précédent : 001B50; suivant : 001B52Densifying a Behavioral Recommender System by Social Networks link prediction methods
Auteurs : Ilham Esslimani ; Armelle Brun ; Anne BoyerSource :
- Social Network Analysis and Mining [ 1869-5450 ] ; 2011-07-14.
Abstract
Recommender systems are widely used for personalization of information on the Web and information retrieval systems. Collaborative Filtering (CF) is the most popular recommendation technique. However, Classical CF (CCF) systems use only direct links and common features to model relationships between users. This paper presents a new Densified Behavioral Network based Collaborative Filtering model (D-BNCF), based on the BNCF approach that uses navigational patterns to model relationships between users. D-BNCF exploits additionally social networks techniques, such as prediction link methods, to discover new links throughout the behavioral network. The final aim is the involvement of these new links in prediction generation to improve the quality of recommendations. The approach proposed is evaluated in terms of accuracy on a real usage dataset. The experimentation shows the benefit of exploiting new links to compute predictions in terms of HMAE. Besides, the evaluation of a combined model (that exploits the more accurate D-BNCF models) shows the interest of combining similarities based on two different link prediction methods and its impact on the accuracy of high predictions.
Url:
DOI: 10.1007/s13278-010-0004-6
Links to Exploration step
Hal:inria-00430331Le document en format XML
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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-300291" type="indirect"><org type="institution" xml:id="struct-300291" status="OLD"><orgName>Université Henri Poincaré - Nancy 1</orgName><orgName type="acronym">UHP</orgName><date type="end">2011-12-31</date><desc><address><addrLine>24-30 rue Lionnois, BP 60120, 54 003 NANCY cedex, France</addrLine><country key="FR"></country></address></desc></org></tutelle><tutelle active="#struct-300292" type="indirect"><org type="institution" xml:id="struct-300292" status="OLD"><orgName>Université Nancy 2</orgName><date type="end">2011-12-31</date><desc><address><addrLine>91 avenue de la Libération, BP 454, 54001 Nancy cedex</addrLine><country key="FR"></country></address></desc></org></tutelle><tutelle active="#struct-300293" type="indirect"><org type="institution" xml:id="struct-300293" status="OLD"><orgName>Institut National Polytechnique de Lorraine</orgName><orgName type="acronym">INPL</orgName><date type="end">2011-12-31</date><desc><address><country key="FR"></country></address></desc></org></tutelle></tutelles></hal:affiliation></affiliation></author></analytic><idno type="DOI">10.1007/s13278-010-0004-6</idno><series><title level="j">Social Network Analysis and Mining</title><idno type="ISSN">1869-5450</idno><imprint><date type="datePub">2011-07-14</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">Recommender systems are widely used for personalization of information on the Web and information retrieval systems. Collaborative Filtering (CF) is the most popular recommendation technique. However, Classical CF (CCF) systems use only direct links and common features to model relationships between users. This paper presents a new Densified Behavioral Network based Collaborative Filtering model (D-BNCF), based on the BNCF approach that uses navigational patterns to model relationships between users. D-BNCF exploits additionally social networks techniques, such as prediction link methods, to discover new links throughout the behavioral network. The final aim is the involvement of these new links in prediction generation to improve the quality of recommendations. The approach proposed is evaluated in terms of accuracy on a real usage dataset. The experimentation shows the benefit of exploiting new links to compute predictions in terms of HMAE. Besides, the evaluation of a combined model (that exploits the more accurate D-BNCF models) shows the interest of combining similarities based on two different link prediction methods and its impact on the accuracy of high predictions.</div></front></TEI><hal api="V3"><titleStmt><title xml:lang="en">Densifying a Behavioral Recommender System by Social Networks link prediction methods</title><author role="aut"><persName><forename type="first">Ilham</forename><surname>Esslimani</surname></persName><email>ilham.esslimani@loria.fr</email><idno type="halauthor">366279</idno><affiliation ref="#struct-54311"></affiliation></author><author role="aut"><persName><forename type="first">Armelle</forename><surname>Brun</surname></persName><email>armelle.brun@loria.fr</email><idno type="halauthor">134733</idno><orgName ref="#struct-300292"></orgName><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">63903</idno><affiliation ref="#struct-54311"></affiliation></author><editor role="depositor"><persName><forename>Armelle</forename><surname>Brun</surname></persName><email>armelle.brun@loria.fr</email></editor></titleStmt><editionStmt><edition n="v1" type="current"><date type="whenSubmitted">2009-11-06 14:54:25</date><date type="whenModified">2016-05-19 01:05:20</date><date type="whenReleased">2009-11-06 14:54:25</date><date type="whenProduced">2011-07-14</date></edition><respStmt><resp>contributor</resp><name key="113400"><persName><forename>Armelle</forename><surname>Brun</surname></persName><email>armelle.brun@loria.fr</email></name></respStmt></editionStmt><publicationStmt><distributor>CCSD</distributor><idno type="halId">inria-00430331</idno><idno type="halUri">https://hal.inria.fr/inria-00430331</idno><idno type="halBibtex">esslimani:inria-00430331</idno><idno type="halRefHtml">Social Network Analysis and Mining, Springer, 2011, 1 (3), pp.159--172. <10.1007/s13278-010-0004-6></idno><idno type="halRef">Social Network Analysis and Mining, Springer, 2011, 1 (3), pp.159--172. <10.1007/s13278-010-0004-6></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="LORIA2">Publications du LORIA</idno><idno type="stamp" n="LABO-LORIA-SET" p="LORIA">LABO-LORIA-SET</idno><idno type="stamp" n="LORIA">LORIA - Laboratoire Lorrain de Recherche en Informatique et ses Applications</idno><idno type="stamp" n="LORIA-CSAI" p="LORIA">Systèmes complexes et intelligence artificielle</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="popular" n="0">No</note><note type="peer" n="1">Yes</note></notesStmt><sourceDesc><biblStruct><analytic><title xml:lang="en">Densifying a Behavioral Recommender System by Social Networks link prediction methods</title><author role="aut"><persName><forename type="first">Ilham</forename><surname>Esslimani</surname></persName><email>ilham.esslimani@loria.fr</email><idno type="halAuthorId">366279</idno><affiliation ref="#struct-54311"></affiliation></author><author role="aut"><persName><forename type="first">Armelle</forename><surname>Brun</surname></persName><email>armelle.brun@loria.fr</email><idno type="halAuthorId">134733</idno><orgName ref="#struct-300292"></orgName><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">63903</idno><affiliation ref="#struct-54311"></affiliation></author></analytic><monogr><idno type="halJournalId" status="VALID">102590</idno><idno type="issn">1869-5450</idno><idno type="eissn">1869-5469</idno><title level="j">Social Network Analysis and Mining</title><title level="m">Advances in Social Networks Analysis and Mining</title><editor>Springer</editor><imprint><publisher>Springer</publisher><biblScope unit="volume">1</biblScope><biblScope unit="issue">3</biblScope><biblScope unit="pp">159--172</biblScope><date type="datePub">2011-07-14</date></imprint></monogr><idno type="doi">10.1007/s13278-010-0004-6</idno></biblStruct></sourceDesc><profileDesc><langUsage><language ident="en">English</language></langUsage><textClass><classCode scheme="halDomain" n="info.info-ai">Computer Science [cs]/Artificial Intelligence [cs.AI]</classCode><classCode scheme="halDomain" n="info.info-ir">Computer Science [cs]/Information Retrieval [cs.IR]</classCode><classCode scheme="halTypology" n="ART">Journal articles</classCode></textClass><abstract xml:lang="en">Recommender systems are widely used for personalization of information on the Web and information retrieval systems. Collaborative Filtering (CF) is the most popular recommendation technique. However, Classical CF (CCF) systems use only direct links and common features to model relationships between users. This paper presents a new Densified Behavioral Network based Collaborative Filtering model (D-BNCF), based on the BNCF approach that uses navigational patterns to model relationships between users. D-BNCF exploits additionally social networks techniques, such as prediction link methods, to discover new links throughout the behavioral network. The final aim is the involvement of these new links in prediction generation to improve the quality of recommendations. The approach proposed is evaluated in terms of accuracy on a real usage dataset. The experimentation shows the benefit of exploiting new links to compute predictions in terms of HMAE. Besides, the evaluation of a combined model (that exploits the more accurate D-BNCF models) shows the interest of combining similarities based on two different link prediction methods and its impact on the accuracy of high predictions.</abstract></profileDesc></hal></record>Pour manipuler ce document sous Unix (Dilib)
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