Analyzing temporal dynamics in Twitter profiles for personalized recommendations in the social web

Published 15 June 2011

Fabian Abel, Qi Gao, Geert‐Jan Houben, Ke Tao

Citations122

TL;DR

This paper investigates user modeling strategies for inferring personal interest profiles from Social Web interactions and compares different strategies for creating user profiles based on the Twitter messages a user has published and study how these profiles change over time.

Abstract

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 culture, 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 profiles from Social Web interactions. In particular, we analyze individual micro-blogging activities on Twitter. We compare different strategies for creating user profiles based on the Twitter messages a user has published and study how these profiles 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 profiles allow for the best performance.

Keywords

Computer SciencePhysics and Astronomy

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