Analyzing Temporal Dynamics in Twitter Profiles for Personalized Recommendations in the Social Web

author: Fabian Abel, Leibniz University of Hannover
published: July 19, 2011,   recorded: June 2011,   views: 3992


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Social Web describes a new culture of participation on the Web where more and more people actively participate in publishing and organizing Web content. As part of this cul- ture, people leave a variety of traces when interacting with (other people via) Social Web systems. In this paper, we investigate user modeling strategies for inferring personal interest pro les from Social Web interactions. In particular, we analyze individual micro-blogging activities on Twitter. We compare di erent strategies for creating user pro les based on the Twitter messages a user has published and study how these pro les change over time. Moreover, we evaluate the quality of the user modeling strategies in the context of personalized recommender systems and show that those strategies which consider the temporal dynamics of the individual pro les allow for the best performance.

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