An algorithmic framework for performing collaborative filtering
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
Incorporating contextual information in recommender systems using a multidimensional approach
ACM Transactions on Information Systems (TOIS)
Time weight collaborative filtering
Proceedings of the 14th ACM international conference on Information and knowledge management
Using Location for Personalized POI Recommendations in Mobile Environments
SAINT '06 Proceedings of the International Symposium on Applications on Internet
Mining interesting locations and travel sequences from GPS trajectories
Proceedings of the 18th international conference on World wide web
Collaborative filtering with temporal dynamics
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
On social networks and collaborative recommendation
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Learning to recommend with trust and distrust relationships
Proceedings of the third ACM conference on Recommender systems
A survey of collaborative filtering techniques
Advances in Artificial Intelligence
Temporal recommendation on graphs via long- and short-term preference fusion
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Location recommendation for location-based social networks
Proceedings of the 18th SIGSPATIAL International Conference on Advances in Geographic Information Systems
Mining significant semantic locations from GPS data
Proceedings of the VLDB Endowment
CLR: a collaborative location recommendation framework based on co-clustering
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
Exploiting geographical influence for collaborative point-of-interest recommendation
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
Friendship and mobility: user movement in location-based social networks
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Proceedings of the 19th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
Finding wormholes with flickr geotags
ECIR'2010 Proceedings of the 32nd European conference on Advances in Information Retrieval
Exploring social influence for recommendation: a generative model approach
SIGIR '12 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
Proceedings of the Fifth International Conference on Management of Emergent Digital EcoSystems
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The availability of user check-in data in large volume from the rapid growing location based social networks (LBSNs) enables many important location-aware services to users. Point-of-interest (POI) recommendation is one of such services, which is to recommend places where users have not visited before. Several techniques have been recently proposed for the recommendation service. However, no existing work has considered the temporal information for POI recommendations in LBSNs. We believe that time plays an important role in POI recommendations because most users tend to visit different places at different time in a day, \eg visiting a restaurant at noon and visiting a bar at night. In this paper, we define a new problem, namely, the time-aware POI recommendation, to recommend POIs for a given user at a specified time in a day. To solve the problem, we develop a collaborative recommendation model that is able to incorporate temporal information. Moreover, based on the observation that users tend to visit nearby POIs, we further enhance the recommendation model by considering geographical information. Our experimental results on two real-world datasets show that the proposed approach outperforms the state-of-the-art POI recommendation methods substantially.