Personalized Event Recommendations Using Social Networks

In recent years we have observed a significant increase in the popularity of location-based social networks for exchanging news and experiences, sharing location information, or publishing real world events. One important challenge in such networks is to understand human crowd mobility behavior based on user social activities and interactions. In this paper we introduce PRESENT, our middleware that utilizes a Mixed Markov Model to extract the behavioral patterns of the users in social groups, to make personalized event recommendations. Our detailed experimental evaluation, using data from the Meet up location-based social network, illustrates that our approach is efficient, practical and achieves an average prediction for the user attendance of over 73%.