Personalized Group Recommender Systems for Location- and Event-Based Social Networks

Abstract

Location-Based Social Networks (LBSNs) such as Foursquare, Google+ Local, and so on, and Event-Based Social Networks (EBSNs) such as Meetup, Plancast, and so on, have become popular platforms for users to plan, organize, and attend social events with friends and acquaintances. These LBSNs and EBSNs provide rich content such as online and offline user interactions, location/event descriptions that can be leveraged for personalized group recommendations. In this article, we propose novel Collaborative Filtering-based Bayesian models to capture the location or event semantics and group dynamics such as user interactions, user group membership, user influence, and the like for personalized group recommendations. Empirical experiments on two large real-world datasets (Gowalla LBSN dataset and Meetup EBSN dataset) show that our models outperform the state-of-the-art group recommender systems. We discuss the group characteristics of our datasets and show that modeling of group dynamics learns better group preferences than aggregating individual user preferences. Moreover, our model provides human interpretable results that can be used to understand group participation behavior and location/event popularity.