Toward Word Embedding for Personalized Information Retrieval
| Autor: | Ould Amer, Nawal, Mulhem, Philippe, Géry, Mathias |
|---|---|
| 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 |
| Jazyk: | angličtina |
| Rok vydání: | 2016 |
| Předmět: |
FOS: Computer and information sciences
Query expansion Computer Science - Computation and Language Personalization Word Embedding [INFO.INFO-IR]Computer Science [cs]/Information Retrieval [cs.IR] InformationSystems_INFORMATIONSTORAGEANDRETRIEVAL word2vec Social Book Search Computation and Language (cs.CL) Information Retrieval (cs.IR) Computer Science - Information Retrieval |
| Zdroj: | Proceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval Neu-IR: The SIGIR 2016 Workshop on Neural Information Retrieval Neu-IR: The SIGIR 2016 Workshop on Neural Information Retrieval, Jul 2016, Pisa, Italy |
| Popis: | International audience; This paper presents preliminary works on using Word Embedding (word2vec) for query expansion in the context of Personalized Information Retrieval. Traditionally, word em-beddings are learned on a general corpus, like Wikipedia. In this work we try to personalize the word embeddings learning , by achieving the learning on the user's profile. The word embeddings are then in the same context than the user interests. Our proposal is evaluated on the CLEF Social Book Search 2016 collection. The results obtained show that some efforts should be made in the way to apply Word Embedding in the context of Personalized Information Retrieval. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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