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Lyon
Aug. 22, 2011 to Aug. 27, 2011
ISBN: 978-1-4577-1373-6
pp: 100-103
ABSTRACT
In this paper, we study user modeling on Twitter and investigate the interplay between personal interests and public trends. To generate semantically meaningful user profiles, we present a framework that allows us to enrich the semantics of individual Twitter messages and features user modeling as well as trend modeling strategies. These profiles can be re-used in other applications for (trend-aware) personalization. Given a large Twitter dataset, we analyze the characteristics of user and trend profiles and evaluate the quality of the profiles in the context of a personalized news recommendation system. We show that personal interests are more important for the recommendation process than public trends and that by combining both types of profiles we can further improve recommendation quality.
INDEX TERMS
twitter, user modeling, trend modeling, personalized news recommendation, social web
CITATION
Qi Gao, Fabian Abel, Geert-Jan Houben, Ke Tao, "Interweaving Trend and User Modeling for Personalized News Recommendation", WI-IAT, 2011, 2011 IEEE/WIC/ACM International Joint Conferences on Web Intelligence (WI) and Intelligent Agent Technologies, 2011 IEEE/WIC/ACM International Joint Conferences on Web Intelligence (WI) and Intelligent Agent Technologies 2011, pp. 100-103, doi:10.1109/WI-IAT.2011.74
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