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In this paper, we present our contribution in INEX 2015 Social Book Search Track. This track aims to exploit social information (users reviews, ratings, etc. . . ) from LibraryThing and Amazon collections. We used traditional information retrieval models, namely, InL2 and the Sequential Dependence Model (SDM) and tested their combination. We integrated tools from natural language processing (NLP) and approaches based on graph analysis to improve the recommendation performances.  23 mars 2016 à 06:58 Ivan Monnier
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A new combination of multiple Information Retrieval approaches are proposed for book recommendation based on complex users’ queries. We used different theoretical retrieval models: probabilistic as InL2 (Divergence From Randomness model) and language models and tested their interpolated combination. We considered the application of a graph based algorithm in a new retrieval approach to related document network comprised of social links. We called Directed Graph of Documents (DGD) a network constructed with documents and social information provided from each one of them. Specifically, this work tackles the problem of book recommendation in the context of CLEF Labs precisely Social Book Search track. We established a specific strategy for queries searching after separating query set into two genres “Analogue” and “Non-Analogue” after analyzing users’ needs. Series of reranking experiments demonstrate that combining retrieval models and exploiting linked documents for retrieving yield significant improvements in terms of standard ranked retrieval metrics. These results extend the applicability of link analysis algorithms to different environments.  23 mars 2016 à 06:57 Ivan Monnier
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A combination of multiple information retrieval approaches is proposed for the purpose of book recommendation. In this paper, book recommendation is based on complex user's query. We used different theoretical retrieval models: probabilistic as InL2 (Divergence from Randomness model) and language model and tested their interpolated combination. Graph analysis algorithms such as PageRank have been successful in Web environments. We consider the application of this algorithm in a new retrieval approach to related document network comprised of social links. We called Directed Graph of Documents (DGD) a network constructed with documents and social information provided from each one of them. Specifically, this work tackles the problem of book recommendation in the context of INEX (Initiative for the Evaluation of XML retrieval) Social Book Search track. A series of reranking experiments demonstrate that combining retrieval models yields significant improvements in terms of standard ranked retrieval metrics. These results extend the applicability of link analysis algorithms to different environments.  23 mars 2016 à 06:56 Ivan Monnier
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Le Salon Innovative SHS a pour but de présenter les savoir-faire des unités de recherche en sciences humaines et sociales et valoriser leurs acquis vers le monde économique et social dans les domaines du patrimoine, de l’analyse territoriales, de l’éducation ou de la santé. Tout au long du salon, des animations et des tables rondes seront organisées afin de dynamiser les échanges entre scientifiques, partenaires économiques et visiteurs.  23 mars 2016 à 06:55 Ivan Monnier
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—A new combination of multiple Information Retrieval approaches are proposed for book recommendation based on complex users’ queries. We used different theoretical retrieval models: probabilistic as InL2 (Divergence From Randomness model) and language models and tested their interpolated combination. We considered the application of a graph based algorithm in a new retrieval approach to related document network comprised of social links. We called Directed Graph of Documents (DGD) a network constructed with documents and social information provided from each one of them. Specifically, this work tackles the problem of book recommendation in the context of CLEF Labs precisely Social Book Search track. We established a specific strategy for queries searching after separating query set into two genres “Analogue” and “Analogue” after analyzing users’ needs. Series of reranking experiments demonstrate that combining retrieval models and exploiting linked documents for retrieving yield significant improvements in terms of standard ranked retrieval metrics. These results extend the applicability of link analysis algorithms to different environments.  23 mars 2016 à 06:59 Ivan Monnier
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