Constructing popular routes from uncertain trajectories
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Community discovery and profiling with social messages
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Exploiting enriched contextual information for mobile app classification
Proceedings of the 21st ACM international conference on Information and knowledge management
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
Mining geographic-temporal-semantic patterns in trajectories for location prediction
ACM Transactions on Intelligent Systems and Technology (TIST) - Special Section on Intelligent Mobile Knowledge Discovery and Management Systems and Special Issue on Social Web Mining
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As the worlds of commerce, entertainment, travel, and Internet technology become more inextricably linked, new types of business data become available for creative use and formal analysis. Indeed, this paper provides a study of exploiting online travel information for personalized travel package recommendation. A critical challenge along this line is to address the unique characteristics of travel data, which distinguish travel packages from traditional items for recommendation. To this end, we first analyze the characteristics of the travel packages and develop a Tourist-Area-Season Topic (TAST) model, which can extract the topics conditioned on both the tourists and the intrinsic features (i.e. locations, travel seasons) of the landscapes. Based on this TAST model, we propose a cocktail approach on personalized travel package recommendation. Finally, we evaluate the TAST model and the cocktail approach on real-world travel package data. The experimental results show that the TAST model can effectively capture the unique characteristics of the travel data and the cocktail approach is thus much more effective than traditional recommendation methods for travel package recommendation.