GroupLens: applying collaborative filtering to Usenet news
Communications of the ACM
Automatic resource compilation by analyzing hyperlink structure and associated text
WWW7 Proceedings of the seventh international conference on World Wide Web 7
An algorithmic framework for performing collaborative filtering
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Authoritative sources in a hyperlinked environment
Journal of the ACM (JACM)
Methods and metrics for cold-start recommendations
SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
Using Location for Personalized POI Recommendations in Mobile Environments
SAINT '06 Proceedings of the International Symposium on Applications on Internet
A new similarity measure for collaborative filtering to alleviate the new user cold-starting problem
Information Sciences: an International Journal
SoRec: social recommendation using probabilistic matrix factorization
Proceedings of the 17th ACM conference on Information and knowledge management
Mining interesting locations and travel sequences from GPS trajectories
Proceedings of the 18th international conference on World wide web
The wisdom of the few: a collaborative filtering approach based on expert opinions from the web
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Skyline queries based on user locations and preferences for making location-based recommendations
Proceedings of the 2009 International Workshop on Location Based Social Networks
Collaborative location and activity recommendations with GPS history data
Proceedings of the 19th international conference on World wide web
Bridging the gap between physical location and online social networks
Proceedings of the 12th ACM international conference on Ubiquitous computing
Towards location-based social networking services
Proceedings of the 2nd ACM SIGSPATIAL International Workshop on Location Based Social Networks
Location recommendation for location-based social networks
Proceedings of the 18th SIGSPATIAL International Conference on Advances in Geographic Information Systems
Learning travel recommendations from user-generated GPS traces
ACM Transactions on Intelligent Systems and Technology (TIST)
Mining significant semantic locations from GPS data
Proceedings of the VLDB Endowment
Recommending friends and locations based on individual location history
ACM Transactions on the Web (TWEB)
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
Computing with Spatial Trajectories
Computing with Spatial Trajectories
LARS: A Location-Aware Recommender System
ICDE '12 Proceedings of the 2012 IEEE 28th International Conference on Data Engineering
Location-based recommendation system using Bayesian user's preference model in mobile devices
UIC'07 Proceedings of the 4th international conference on Ubiquitous Intelligence and Computing
Dissecting foursquare venue popularity via random region sampling
Proceedings of the 2012 ACM conference on CoNEXT student workshop
A sentiment-enhanced personalized location recommendation system
Proceedings of the 24th ACM Conference on Hypertext and Social Media
LCARS: a location-content-aware recommender system
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
Geo-spotting: mining online location-based services for optimal retail store placement
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
Combining latent factor model with location features for event-based group recommendation
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
Learning geographical preferences for point-of-interest recommendation
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
Constructing trip routes with user preference from location check-in data
Proceedings of the 2013 ACM conference on Pervasive and ubiquitous computing adjunct publication
Social interactions over geographic-aware multimedia systems
Proceedings of the 21st ACM international conference on Multimedia
Proceedings of the 2nd ACM SIGKDD International Workshop on Urban Computing
Prediction of user location using the radiation model and social check-ins
Proceedings of the 2nd ACM SIGKDD International Workshop on Urban Computing
Redeem with privacy (RWP): privacy protecting framework for geo-social commerce
Proceedings of the 12th ACM workshop on Workshop on privacy in the electronic society
A carpooling recommendation system based on social VANET and geo-social data
Proceedings of the 21st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
GeoRank: an efficient location-aware news feed ranking system
Proceedings of the 21st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
iGSLR: personalized geo-social location recommendation: a kernel density estimation approach
Proceedings of the 21st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
CALBA: capacity-aware location-based advertising in temporary social networks
Proceedings of the 21st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
Location recommendation in location-based social networks using user check-in data
Proceedings of the 21st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
Weighted multi-attribute matching of user-generated points of interest
Proceedings of the 21st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
Geographical and temporal similarity measurement in location-based social networks
Proceedings of the Second ACM SIGSPATIAL International Workshop on Mobile Geographic Information Systems
Efficient Monitoring of Moving Mobile Device Range Queries using Dynamic Safe Regions
Proceedings of International Conference on Advances in Mobile Computing & Multimedia
Customized tour recommendations in urban areas
Proceedings of the 7th ACM international conference on Web search and data mining
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The popularity of location-based social networks provide us with a new platform to understand users' preferences based on their location histories. In this paper, we present a location-based and preference-aware recommender system that offers a particular user a set of venues (such as restaurants) within a geospatial range with the consideration of both: 1) User preferences, which are automatically learned from her location history and 2) Social opinions, which are mined from the location histories of the local experts. This recommender system can facilitate people's travel not only near their living areas but also to a city that is new to them. As a user can only visit a limited number of locations, the user-locations matrix is very sparse, leading to a big challenge to traditional collaborative filtering-based location recommender systems. The problem becomes even more challenging when people travel to a new city. To this end, we propose a novel location recommender system, which consists of two main parts: offline modeling and online recommendation. The offline modeling part models each individual's personal preferences with a weighted category hierarchy (WCH) and infers the expertise of each user in a city with respect to different category of locations according to their location histories using an iterative learning model. The online recommendation part selects candidate local experts in a geospatial range that matches the user's preferences using a preference-aware candidate selection algorithm and then infers a score of the candidate locations based on the opinions of the selected local experts. Finally, the top-k ranked locations are returned as the recommendations for the user. We evaluated our system with a large-scale real dataset collected from Foursquare. The results confirm that our method offers more effective recommendations than baselines, while having a good efficiency of providing location recommendations.