Word Embedding for Social Book Suggestion

Nawal Ould-Amer 1, 2, 3 Philippe Mulhem 1, 3 Mathias Géry 2 Karam Abdulahhad 1
3 MRIM - Modélisation et Recherche d’Information Multimédia [Grenoble]
Inria - Institut National de Recherche en Informatique et en Automatique, LIG - Laboratoire d'Informatique de Grenoble
Abstract : This paper presents the joint work of the Universities of Grenoble and Saint-´ Etienne at CLEF 2016 Social Book Search Suggestion Track. The approaches studied are based on personalization, considering the user's profile in the ranking process. The profile is filtered using Word Embedding, by proposing several ways to handle the generated relationships between terms. We find that tackling the problem of " non-topical " only queries is a great challenge in this case. The official results show that Word Embedding methods are able to improve results in the SBS case.
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Submitted on : Thursday, October 6, 2016 - 11:58:08 AM
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Nawal Ould-Amer, Philippe Mulhem, Mathias Géry, Karam Abdulahhad. Word Embedding for Social Book Suggestion. Clef 2016 Conference, Sep 2016, Evora, Portugal. ⟨ujm-01377072⟩

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