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Université Jean Monnet – Saint-Etienne |  |
Conference papers
Word Embedding for Social Book Suggestion
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.
Cited literature [11 references]
https://hal-ujm.archives-ouvertes.fr/ujm-01377072
Contributor : Mathias Géry <>
Submitted on : Thursday, October 6, 2016 - 11:58:08 AM
Last modification on : Thursday, November 19, 2020 - 1:06:02 PM
Long-term archiving on: : Saturday, January 7, 2017 - 12:53:54 PM
Contributor : Mathias Géry <>
Submitted on : Thursday, October 6, 2016 - 11:58:08 AM
Last modification on : Thursday, November 19, 2020 - 1:06:02 PM
Long-term archiving on: : Saturday, January 7, 2017 - 12:53:54 PM
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word-embedding-social.pdf
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