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- W2745698934 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." @default.
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- W2745698934 date "2016-09-05" @default.
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- W2745698934 title "Word Embedding for Social Book Suggestion" @default.
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