Modeling Group Dynamics for Personalized Group-Event Recommendation

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9021)

Abstract

Event-Based Social Networks (EBSNs) such as Meetup, Plancast, etc., have become popular platforms for users to plan and organize social events with friends and acquaintances. These EBSNs provide rich online and offline user interactions, and rich event content information which can be leveraged for personalized group-event recommendations. In this paper, we propose collaborative-filtering based Bayesian models which captures group dynamics such as user interactions, user-group membership etc., for personalized group-event recommendations. We show that modeling group dynamics learns the group preferences better than aggregating individual user preferences, and that our approach out-performs popular state-of-the-art group recommender systems. Moreover, our model provides interpretable results which can be used to study the group participations and event popularity.

Keywords

Personalized group recommendation Group dynamics Event-Based Social Networks Bayesian models Collaborative filtering 

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.University of Southern CaliforniaLos AngelesUSA

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