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Toward Word Embedding for Personalized Information Retrieval · Around

2016

Toward Word Embedding for Personalized Information Retrieval

Ould-Amer, Nawal, Mulhem, Philippe, Gery, Mathias

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This paper presents preliminary works on using Word Embedding (word2vec) for query 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 achieving the learning on the user's profile.
The word embeddings are then in the same context than the user interests.

Built on

Indexing by latent semantic analysis
S. Deerwester, S. T. Dumais, G. W. Furnas, T. K. Landauer, and R. Harshman · 1990
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Geometry and Meaning
D. Widdows · 2004
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Terrier: A High Performance and Scalable Information Retrieval Platform
I. Ounis, G. Amati, V. Plachouras, B. He, C. Macdonald, and C. Lioma · 2006
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Efficient estimation of word representations in vector space
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T. Mikolov, K. Chen, G. Corrado, and J. Dean · 2013
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