Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
Optimal aggregation algorithms for middleware
PODS '01 Proceedings of the twentieth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Amazon.com Recommendations: Item-to-Item Collaborative Filtering
IEEE Internet Computing
Unifying collaborative and content-based filtering
ICML '04 Proceedings of the twenty-first international conference on Machine learning
IEEE Transactions on Knowledge and Data Engineering
A maximum entropy web recommendation system: combining collaborative and content features
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Using Location for Personalized POI Recommendations in Mobile Environments
SAINT '06 Proceedings of the International Symposium on Applications on Internet
A new approach for combining content-based and collaborative filters
Journal of Intelligent Information Systems
Factorization meets the neighborhood: a multifaceted collaborative filtering model
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
ArnetMiner: extraction and mining of academic social networks
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Collaborative filtering for orkut communities: discovery of user latent behavior
Proceedings of the 18th international conference on World wide web
WhereNext: a location predictor on trajectory pattern mining
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Collaborative location and activity recommendations with GPS history data
Proceedings of the 19th international conference on World wide web
Performance of recommender algorithms on top-n recommendation tasks
Proceedings of the fourth ACM conference on Recommender systems
Location recommendation for location-based social networks
Proceedings of the 18th SIGSPATIAL International Conference on Advances in Geographic Information Systems
Recommender Systems Handbook
Hyper-local, directions-based ranking of places
Proceedings of the VLDB Endowment
Exploiting geographical influence for collaborative point-of-interest recommendation
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
Exploiting place features in link prediction on location-based social networks
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Friendship and mobility: user movement in location-based social networks
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Human mobility, social ties, and link prediction
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
A model for proactivity in mobile, context-aware recommender systems
Proceedings of the fifth ACM conference on Recommender systems
UAI'01 Proceedings of the Seventeenth 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
Factorization Machines with libFM
ACM Transactions on Intelligent Systems and Technology (TIST)
LARS: A Location-Aware Recommender System
ICDE '12 Proceedings of the 2012 IEEE 28th International Conference on Data Engineering
Challenging the long tail recommendation
Proceedings of the VLDB Endowment
Event-based social networks: linking the online and offline social worlds
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Cross-domain collaboration recommendation
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
User location forecasting at points of interest
Proceedings of the 2012 RecSys workshop on Personalizing the local mobile experience
gSCorr: modeling geo-social correlations for new check-ins on location-based social networks
Proceedings of the 21st ACM international conference on Information and knowledge management
Location-based and preference-aware recommendation using sparse geo-social networking data
Proceedings of the 20th International Conference on Advances in Geographic Information Systems
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Newly emerging location-based and event-based social network services provide us with a new platform to understand users' preferences based on their activity history. A user can only visit a limited number of venues/events and most of them are within a limited distance range, so the user-item matrix is very sparse, which creates a big challenge for traditional collaborative filtering-based recommender systems. The problem becomes more challenging when people travel to a new city where they have no activity history. In this paper, we propose LCARS, a location-content-aware recommender system that offers a particular user a set of venues (e.g., restaurants) or events (e.g., concerts and exhibitions) by giving consideration to both personal interest and local preference. This recommender system can facilitate people's travel not only near the area in which they live, but also in a city that is new to them. Specifically, LCARS consists of two components: offline modeling and online recommendation. The offline modeling part, called LCA-LDA, is designed to learn the interest of each individual user and the local preference of each individual city by capturing item co-occurrence patterns and exploiting item contents. The online recommendation part automatically combines the learnt interest of the querying user and the local preference of the querying city to produce the top-k recommendations. To speed up this online process, a scalable query processing technique is developed by extending the classic Threshold Algorithm (TA). We evaluate the performance of our recommender system on two large-scale real data sets, DoubanEvent and Foursquare. The results show the superiority of LCARS in recommending spatial items for users, especially when traveling to new cities, in terms of both effectiveness and efficiency.