Word Embedding for Social Book Suggestion
| Autor: | Ould-Amer, Nawal, Mulhem, Philippe, Géry, Mathias, Abdulahhad, Karam |
|---|---|
| Přispěvatelé: | Laboratoire d'Informatique de Grenoble (LIG ), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), Laboratoire Hubert Curien [Saint Etienne] (LHC), Institut d'Optique Graduate School (IOGS)-Université Jean Monnet [Saint-Étienne] (UJM)-Centre National de la Recherche Scientifique (CNRS), Modélisation et Recherche d’Information Multimédia [Grenoble] (MRIM ), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), ARC6 Région Rhône Alpes, Krisztian Balog, Linda Cappellato, Nicola Ferro, Craig Macdonald |
| Jazyk: | angličtina |
| Rok vydání: | 2016 |
| Předmět: | |
| Zdroj: | CLEF 2016 Working Notes Clef 2016 Conference Clef 2016 Conference, Sep 2016, Evora, Portugal |
| Popis: | International audience; 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|