How Hidden Aspects Can Improve Recommendation?

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.
Type de document :
Communication dans un congrès
6th International Conference, SocInfo 2014, Nov 2014, Barcelone, Spain. Springer, Proceedings of the 6th International Conference, SocInfo 2014, 8851, pp.269-278, 2014, Social Informatics. 〈10.1007/978-3-319-13734-6_19〉
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https://hal-supelec.archives-ouvertes.fr/hal-01105283
Contributeur : Elodie Dubrac <>
Soumis le : mardi 20 janvier 2015 - 10:19:43
Dernière modification le : jeudi 29 mars 2018 - 11:06:05

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Youssef Meguebli, Mounia Kacimi, Bich-Liên Doan, Fabrice Popineau. How Hidden Aspects Can Improve Recommendation?. 6th International Conference, SocInfo 2014, Nov 2014, Barcelone, Spain. Springer, Proceedings of the 6th International Conference, SocInfo 2014, 8851, pp.269-278, 2014, Social Informatics. 〈10.1007/978-3-319-13734-6_19〉. 〈hal-01105283〉

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