Analyzing user modeling on twitter for personalized news recommendations

Authors:
Fabian Abel;Qi Gao;Geert-Jan Houben;Ke Tao
Affiliations:
Web Information Systems, Delft University of Technology, Delft, the Netherlands;Web Information Systems, Delft University of Technology, Delft, the Netherlands;Web Information Systems, Delft University of Technology, Delft, the Netherlands;Web Information Systems, Delft University of Technology, Delft, the Netherlands
Venue:
UMAP'11 Proceedings of the 19th international conference on User modeling, adaption, and personalization
Year:
2011

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Abstract

How can micro-blogging activities on Twitter be leveraged for user modeling and personalization? In this paper we investigate this question and introduce a framework for user modeling on Twitter which enriches the semantics of Twitter messages (tweets) and identifies topics and entities (e.g. persons, events, products) mentioned in tweets. We analyze how strategies for constructing hashtag-based, entity-based or topic-based user profiles benefit from semantic enrichment and explore the temporal dynamics of those profiles. We further measure and compare the performance of the user modeling strategies in context of a personalized news recommendation system. Our results reveal how semantic enrichment enhances the variety and quality of the generated user profiles. Further, we see how the different user modeling strategies impact personalization and discover that the consideration of temporal profile patterns can improve recommendation quality.