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Personalized Parsimonious Language Models for User Modeling in Social Bookmaking Systems

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Abstract

This paper focuses on building accurate profiles of users, based on bookmarking systems. To achieve this goal, we define personalized parsimonious language models that employ three main resources: the tags, the documents tagged by the user and word embeddings that handle general knowledge. Experiments completed on Delicious data show that our proposal outperforms state-of-the-art approaches and non-personalized parsimonious models.
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Dates and versions

ujm-01615362 , version 1 (12-10-2017)

Identifiers

  • HAL Id : ujm-01615362 , version 1

Cite

Nawal Ould Amer, Philippe Mulhem, Mathias Géry. Personalized Parsimonious Language Models for User Modeling in Social Bookmaking Systems. European Conference on Information Retrieval, Apr 2017, Aberdeen, United Kingdom. ⟨ujm-01615362⟩
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