Verbose Query Reduction by Learning to Rank for Social Book Search Track

dc.contributor.authorChaa, Messaoud
dc.contributor.authorNouali, Omar
dc.contributor.authorBellot, Patrice
dc.description.abstractIn this paper, we describe our participation in the INEX 2016 Social Book Search Suggestion Track (SBS). We have exploited machine learning techniques to rank query terms and assign an appropriate weight to each one before applying a probabilistic information retrieval model (BM15). Thereafter, only the top-k terms are used in the matching model. Several features are used to describe each term, such as statistical features, syntactic features and others features like whether the term is present in similar books and in the profile of the topic starter. The model was learned using the 2014 and 2015 topics and tested with the 2016 topics. Our experiments show that our approach improves the search results.fr_FR
dc.identifier.isrnCERIST- DRDSI--16-000000013--DZfr_FR
dc.relation.ispartofRapports de recherche internes
dc.structureGénie Documentairefr_FR
dc.subjectLearning to rankfr_FR
dc.subjectverbose query reductionfr_FR
dc.subjectSocial Book Searchfr_FR
dc.subjectquery term weightingfr_FR
dc.titleVerbose Query Reduction by Learning to Rank for Social Book Search Trackfr_FR
dc.typeTechnical Report