Interest Representation, Enrichment, Dynamics, and Propagation: A Study of the Synergetic Effect of Different User Modeling Dimensions for Personalized Recommendations on Twitter

  • Guangyuan Piao
  • John G. Breslin
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10024)


Microblogging services such as Twitter have been widely adopted due to the highly social nature of interactions they have facilitated. With the rich information generated by users on these services, user modeling aims to acquire knowledge about a user’s interests, which is a fundamental step towards personalization as well as recommendations. To this end, researchers have explored different dimensions such as (1) Interest Representation, (2) Content Enrichment, (3) Temporal Dynamics of user interests, and (4) Interest Propagation using semantic information from a knowledge base such as DBpedia. However, those dimensions of user modeling have largely been studied separately, and there is a lack of research on the synergetic effect of those dimensions for user modeling. In this paper, we address this research gap by investigating 16 different user modeling strategies produced by various combinations of those dimensions. Different user modeling strategies are evaluated in the context of a personalized link recommender system on Twitter. Results show that Interest Representation and Content Enrichment play crucial roles in user modeling, followed by Temporal Dynamics. The user modeling strategy considering Interest Representation, Content Enrichment and Temporal Dynamics provides the best performance among the 16 strategies. On the other hand, Interest Propagation has little effect on user modeling in the case of leveraging a rich Interest Representation or considering Content Enrichment.


User Modeling User Interest Entity Recognition Interest Representation Twitter Dataset 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



This publication has emanated from research conducted with the financial support of Science Foundation Ireland (SFI) under Grant Number SFI/12/RC/2289 (Insight Centre for Data Analytics).


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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  1. 1.Insight Centre for Data Analytics, NUI GalwayGalwayIreland

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