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Conference Papers Year : 2017

Personalized Parsimonious Language Models for User Modeling in Social Bookmaking Systems

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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