Modeling Group Dynamics for Personalized Group-Event Recommendation
- Cite this paper as:
- Purushotham S., Jay Kuo C.C. (2015) Modeling Group Dynamics for Personalized Group-Event Recommendation. In: Agarwal N., Xu K., Osgood N. (eds) Social Computing, Behavioral-Cultural Modeling, and Prediction. SBP 2015. Lecture Notes in Computer Science, vol 9021. Springer, Cham
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.
KeywordsPersonalized group recommendation Group dynamics Event-Based Social Networks Bayesian models Collaborative filtering
Unable to display preview. Download preview PDF.