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International Conference on Social Informatics

SocInfo 2014: Social Informatics pp 269-278 | Cite as

How Hidden Aspects Can Improve Recommendation?

  • Youssef Meguebli
  • Mouna Kacimi
  • Bich-liên Doan
  • Fabrice Popineau
Chapter
  • 3.8k Downloads
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8851)

Abstract

Nowadays, more and more people are using online news platforms as their main source of information about daily life events. Users of such platforms discuss around topics providing new insights and sometimes revealing hidden aspects about topics. The valuable information provided by users needs to be exploited to improve the accuracy of news recommendation and thus keep users always motivated to provide comments. However, exploiting user generated content is very challenging due its noisy nature. In this paper, we address this problem by proposing a novel news recommendation system that (1) enrich the profile of news article with user generated content, (2) deal with noisy contents by proposing a ranking model for users’ comments, and (3) propose a diversification model for comments to remove redundancies and provide a wide coverage of topic aspects. The results show that our approach outperforms baseline approaches achieving high accuracy.

Keywords

News recommendation Opinion mining Diversification 
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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Youssef Meguebli
    • 1
  • Mouna Kacimi
    • 2
  • Bich-liên Doan
    • 1
  • Fabrice Popineau
    • 1
  1. 1.SUPELEC Systems Sciences (E3S)Gif sur YvetteFrance
  2. 2.Free University of Bozen-BolzanoItaly

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