Proceedings of the 10th international conference on Intelligent user interfaces
IEEE Transactions on Knowledge and Data Engineering
Trust-aware recommender systems
Proceedings of the 2007 ACM conference on Recommender systems
TrustWalker: a random walk model for combining trust-based and item-based recommendation
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Learning to recommend with social trust ensemble
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Trust based recommender system for the semantic web
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Power-Law Distributions in Empirical Data
SIAM Review
Twitter power: Tweets as electronic word of mouth
Journal of the American Society for Information Science and Technology
Characterizing debate performance via aggregated twitter sentiment
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Collaborative location and activity recommendations with GPS history data
Proceedings of the 19th international conference on World wide web
A matrix factorization technique with trust propagation for recommendation in social networks
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
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)
Sentiment knowledge discovery in twitter streaming data
DS'10 Proceedings of the 13th international conference on Discovery science
We feel fine and searching the emotional web
Proceedings of the fourth ACM international conference on Web search and data mining
Recommender systems with social regularization
Proceedings of the fourth ACM international conference on Web search and data mining
Journal of the American Society for Information Science and Technology
Towards detecting influenza epidemics by analyzing Twitter messages
Proceedings of the First Workshop on Social Media Analytics
A recommendation system for spots in location-based online social networks
Proceedings of the 4th Workshop on Social Network Systems
Exploiting geographical influence for collaborative point-of-interest recommendation
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
A transitivity aware matrix factorization model for recommendation in social networks
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Three
Sentiment strength detection for the social web
Journal of the American Society for Information Science and Technology
A Random Walk around the City: New Venue Recommendation in Location-Based Social Networks
SOCIALCOM-PASSAT '12 Proceedings of the 2012 ASE/IEEE International Conference on Social Computing and 2012 ASE/IEEE International Conference on Privacy, Security, Risk and Trust
Location-based and preference-aware recommendation using sparse geo-social networking data
Proceedings of the 20th International Conference on Advances in Geographic Information Systems
Fine-grained preference-aware location search leveraging crowdsourced digital footprints from LBSNs
Proceedings of the 2013 ACM international joint conference on Pervasive and ubiquitous computing
Exploring temporal effects for location recommendation on location-based social networks
Proceedings of the 7th ACM conference on Recommender systems
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Although online recommendation systems such as recommendation of movies or music have been systematically studied in the past decade, location recommendation in Location Based Social Networks (LBSNs) is not well investigated yet. In LBSNs, users can check in and leave tips commenting on a venue. These two heterogeneous data sources both describe users' preference of venues. However, in current research work, only users' check-in behavior is considered in users' location preference model, users' tips on venues are seldom investigated yet. Moreover, while existing work mainly considers social influence in recommendation, we argue that considering venue similarity can further improve the recommendation performance. In this research, we ameliorate location recommendation by enhancing not only the user location preference model but also recommendation algorithm. First, we propose a hybrid user location preference model by combining the preference extracted from check-ins and text-based tips which are processed using sentiment analysis techniques. Second, we develop a location based social matrix factorization algorithm that takes both user social influence and venue similarity influence into account in location recommendation. Using two datasets extracted from the location based social networks Foursquare, experiment results demonstrate that the proposed hybrid preference model can better characterize user preference by maintaining the preference consistency, and the proposed algorithm outperforms the state-of-the-art methods.