UMAP '09 Proceedings of the 17th International Conference on User Modeling, Adaptation, and Personalization: formerly UM and AH
Enhancing Mobile Recommender Systems with Activity Inference
UMAP '09 Proceedings of the 17th International Conference on User Modeling, Adaptation, and Personalization: formerly UM and AH
Location recommendation for location-based social networks
Proceedings of the 18th SIGSPATIAL International Conference on Advances in Geographic Information Systems
NextPlace: a spatio-temporal prediction framework for pervasive systems
Pervasive'11 Proceedings of the 9th international conference on Pervasive computing
Proceedings of the 13th International Conference on Human Computer Interaction with Mobile Devices and Services
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Location-based Social Networks (LBSN) such as Foursquare are becoming an increasingly popular social media, where users share their location and activities with other users mainly using smartphones and Internet of Things. Data logged by LBSNs, such as Foursquare user check-in events, can be used to derive user models and improve the context-awareness and efficiency of various applications like recommender systems. In particular, activity duration is one important aspect of user behavior that we can derive from LBSNs to improve the timing of recommendation sent to users. These durations are not otherwise directly available from LBSNs, partial GPS-enabled tracking or explicit recording due to various practical constraints. From a Four-square dataset with about 3.7 million users and 300 million check-ins, we observed patterns which inspired us to design methods to determine the duration of user activities. We discuss preliminary results and outline plans for thorough evaluations and future research.