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This paper presents preliminary works on using Word Embedding (word2vec) forquery expansion in the context of Personalized Information Retrieval.Traditionally, word embeddings are learned on a general corpus, like Wikipedia.In this work we try to personalize the word embeddings learning, by achievingthe learning on the user's profile. The word embeddings are then in the samecontext than the user interests. Our proposal is evaluated on the CLEF SocialBook Search 2016 collection. The results obtained show that some efforts shouldbe made in the way to apply Word Embedding in the context of PersonalizedInformation Retrieval.

Toward Word Embedding for Personalized Information Retrieval