Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
Collaborative future event recommendation
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Recommending Social Events from Mobile Phone Location Data
ICDM '10 Proceedings of the 2010 IEEE International Conference on Data Mining
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
CityVoyager: an outdoor recommendation system based on user location history
UIC'06 Proceedings of the Third international conference on Ubiquitous Intelligence and Computing
Ads and the city: considering geographic distance goes a long way
Proceedings of the sixth ACM conference on Recommender systems
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The ever blurring line between online interactions and physical encounters presents an interesting challenge when recommending events. Events created on social networking sites may have ambiguous location scope. The location information provided may be fuzzy or non existent and additionally the reach and radius of interest in the event can vary greatly. In this work, we identify four categories of events: global, location dependent and socially independent, socially dependent and location independent, and location and socially dependent. We classify events from an organizations internal event management service where the location of the event is unknown, but the location of the attendees are known in order to improve scoping of event recommendations. Our results, investigate the impact of ignoring location properties when recommending events using classic collaborative filtering techniques. Additionally, once global and socially independent events are identified, they can be used to provide recommendations to new users, addressing the cold-start problem.