A Low-Order Markov Model integrating Long-Distance Histories for Collaborative Recommender Systems
Identifieur interne : 000285 ( Hal/Curation ); précédent : 000284; suivant : 000286A Low-Order Markov Model integrating Long-Distance Histories for Collaborative Recommender Systems
Auteurs : Geoffray Bonnin [France] ; Armelle Brun [France] ; Anne Boyer [France]Source :
Abstract
Recommender systems provide users with pertinent resources according their context and their profiles, by applying statistical and knowledge discovery techniques. This paper describes a new approach of generating suitable recommendations based on the active user's navigation stream, by considering long and short-distance resources in the history with a tractable model. The Skipping Based Recommender we propose uses Markov models inspired from the ones used in language modeling while integrating skipping techniques to handle noise during navigation. Weighting schemes are also used to alleviate the importance of distant resources. This recommender has also the characteristic to be anytime. It has been tested on a browsing dataset extracted from Intranet logs provided by a French bank. Results show that the use of exponential decay weighting schemes when taking into account non contiguous resources to compute recommendations enhances the accuracy. Moreover, the skipping variant we propose provides a high accuracy while being less complex than state of the art variants.
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This paper describes a new approach of generating suitable recommendations based on the active user's navigation stream, by considering long and short-distance resources in the history with a tractable model. The Skipping Based Recommender we propose uses Markov models inspired from the ones used in language modeling while integrating skipping techniques to handle noise during navigation. Weighting schemes are also used to alleviate the importance of distant resources. This recommender has also the characteristic to be anytime. It has been tested on a browsing dataset extracted from Intranet logs provided by a French bank. Results show that the use of exponential decay weighting schemes when taking into account non contiguous resources to compute recommendations enhances the accuracy. Moreover, the skipping variant we propose provides a high accuracy while being less complex than state of the art variants.</div></front></TEI><hal api="V3"><titleStmt><title xml:lang="en">A Low-Order Markov Model integrating Long-Distance Histories for Collaborative Recommender Systems</title><author role="aut"><persName><forename type="first">Geoffray</forename><surname>Bonnin</surname></persName><email>geoffray.bonnin@loria.fr</email><idno type="halauthor">361785</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">66694</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">63903</idno><affiliation ref="#struct-54311"></affiliation></author><editor role="depositor"><persName><forename>Geoffray</forename><surname>Bonnin</surname></persName><email>geoffray.bonnin@loria.fr</email></editor></titleStmt><editionStmt><edition n="v1" type="current"><date type="whenSubmitted">2008-11-25 14:13:25</date><date type="whenModified">2016-05-19 01:05:36</date><date type="whenReleased">2008-11-25 14:13:25</date><date type="whenProduced">2009-02-08</date></edition><respStmt><resp>contributor</resp><name key="132087"><persName><forename>Geoffray</forename><surname>Bonnin</surname></persName><email>geoffray.bonnin@loria.fr</email></name></respStmt></editionStmt><publicationStmt><distributor>CCSD</distributor><idno type="halId">inria-00341537</idno><idno type="halUri">https://hal.inria.fr/inria-00341537</idno><idno type="halBibtex">bonnin:inria-00341537</idno><idno type="halRefHtml">International Conference on Intelligent User Interfaces (IUI), Feb 2009, Sanibel Island, United States. 2009</idno><idno type="halRef">International Conference on Intelligent User Interfaces (IUI), Feb 2009, Sanibel Island, United States. 2009</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="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="en">A Low-Order Markov Model integrating Long-Distance Histories for Collaborative Recommender Systems</title><author role="aut"><persName><forename type="first">Geoffray</forename><surname>Bonnin</surname></persName><email>geoffray.bonnin@loria.fr</email><idno type="halAuthorId">361785</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">66694</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">63903</idno><affiliation ref="#struct-54311"></affiliation></author></analytic><monogr><meeting><title>International Conference on Intelligent User Interfaces (IUI)</title><date type="start">2009-02-08</date><settlement>Sanibel Island</settlement><country key="US">United States</country></meeting><imprint><date type="datePub">2009</date></imprint></monogr></biblStruct></sourceDesc><profileDesc><langUsage><language ident="en">English</language></langUsage><textClass><keywords scheme="author"><term xml:lang="da">Recommender systems</term><term xml:lang="da">Markov models</term><term xml:lang="da">Skipping</term><term xml:lang="da">Weighting schemes</term></keywords><classCode scheme="acm" n="H.3.3"></classCode><classCode scheme="halDomain" n="info.info-cl">Computer Science [cs]/Computation and Language [cs.CL]</classCode><classCode scheme="halDomain" n="info.info-wb">Computer Science [cs]/Web</classCode><classCode scheme="halDomain" n="info.info-ir">Computer Science [cs]/Information Retrieval [cs.IR]</classCode><classCode scheme="halTypology" n="COMM">Conference papers</classCode></textClass><abstract xml:lang="en">Recommender systems provide users with pertinent resources according their context and their profiles, by applying statistical and knowledge discovery techniques. This paper describes a new approach of generating suitable recommendations based on the active user's navigation stream, by considering long and short-distance resources in the history with a tractable model. The Skipping Based Recommender we propose uses Markov models inspired from the ones used in language modeling while integrating skipping techniques to handle noise during navigation. Weighting schemes are also used to alleviate the importance of distant resources. This recommender has also the characteristic to be anytime. It has been tested on a browsing dataset extracted from Intranet logs provided by a French bank. Results show that the use of exponential decay weighting schemes when taking into account non contiguous resources to compute recommendations enhances the accuracy. Moreover, the skipping variant we propose provides a high accuracy while being less complex than state of the art variants.</abstract></profileDesc></hal></record>Pour manipuler ce document sous Unix (Dilib)
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