Densifying a Behavioral Recommender System by Social Networks link prediction methods
Identifieur interne : 002228 ( Main/Exploration ); précédent : 002227; suivant : 002229Densifying a Behavioral Recommender System by Social Networks link prediction methods
Auteurs : Ilham Esslimani [France] ; Armelle Brun [France] ; Anne Boyer [France]Source :
- 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
Affiliations:
- France
- Grand Est, Lorraine (région)
- Nancy
- Institut national polytechnique de Lorraine, Université Nancy 2, Université de Lorraine
Links toward previous steps (curation, corpus...)
- to stream Hal, to step Corpus: 001B51
- to stream Hal, to step Curation: 001B51
- to stream Hal, to step Checkpoint: 001C92
- to stream Main, to step Merge: 002272
- to stream Main, to step Curation: 002228
Le document en format XML
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type="url">http://www.cnrs.fr/</ref></desc></org></tutelle><tutelle active="#struct-300009" type="indirect"><org type="institution" xml:id="struct-300009" status="VALID"><orgName>Institut National de Recherche en Informatique et en Automatique</orgName><orgName type="acronym">Inria</orgName><desc><address><addrLine>Domaine de VoluceauRocquencourt - 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><country>France</country><placeName><settlement type="city">Nancy</settlement><region type="region" nuts="2">Grand Est</region><region type="old region" nuts="2">Lorraine (région)</region></placeName><orgName type="university">Université Nancy 2</orgName><orgName type="institution" wicri:auto="newGroup">Université de Lorraine</orgName><placeName><settlement type="city">Nancy</settlement><region type="region" nuts="2">Grand Est</region><region type="old region" nuts="2">Lorraine (région)</region></placeName><orgName type="university">Institut national polytechnique de Lorraine</orgName><orgName type="institution" wicri:auto="newGroup">Université de Lorraine</orgName></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><affiliations><list><country><li>France</li></country><region><li>Grand Est</li><li>Lorraine (région)</li></region><settlement><li>Nancy</li></settlement><orgName><li>Institut national polytechnique de Lorraine</li><li>Université Nancy 2</li><li>Université de Lorraine</li></orgName></list><tree><country name="France"><region name="Grand Est"><name sortKey="Esslimani, Ilham" sort="Esslimani, Ilham" uniqKey="Esslimani I" first="Ilham" last="Esslimani">Ilham Esslimani</name></region><name sortKey="Boyer, Anne" sort="Boyer, Anne" uniqKey="Boyer A" first="Anne" last="Boyer">Anne Boyer</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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