Cyberguide: a mobile context-aware tour guide
Wireless Networks - Special issue: mobile computing and networking: selected papers from MobiCom '96
Authoritative sources in a hyperlinked environment
Journal of the ACM (JACM)
Agglomerative clustering of a search engine query log
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
CubeSVD: a novel approach to personalized Web search
WWW '05 Proceedings of the 14th international conference on World Wide Web
Unifying user-based and item-based collaborative filtering approaches by similarity fusion
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Mining user similarity based on location history
Proceedings of the 16th ACM SIGSPATIAL international conference on Advances in geographic information systems
Mining interesting locations and travel sequences from GPS trajectories
Proceedings of the 18th international conference on World wide web
Personalized Location-Based Recommendation Services for Tour Planning in Mobile Tourism Applications
EC-Web 2009 Proceedings of the 10th International Conference on E-Commerce and Web Technologies
Collaborative location and activity recommendations with GPS history data
Proceedings of the 19th international conference on World wide web
CityVoyager: an outdoor recommendation system based on user location history
UIC'06 Proceedings of the Third international conference on Ubiquitous Intelligence and Computing
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
Geo-social recommendations based on incremental tensor reduction and local path traversal
Proceedings of the 3rd ACM SIGSPATIAL International Workshop on Location-Based Social Networks
Urban point-of-interest recommendation by mining user check-in behaviors
Proceedings of the ACM SIGKDD International Workshop on Urban Computing
Geo topic model: joint modeling of user's activity area and interests for location recommendation
Proceedings of the sixth ACM international conference on Web search and data mining
Autonomous place naming system using opportunistic crowdsensing and knowledge from crowdsourcing
Proceedings of the 12th international conference on Information processing in sensor networks
A general collaborative filtering framework based on matrix bordered block diagonal forms
Proceedings of the 24th ACM Conference on Hypertext and Social Media
Time-aware point-of-interest recommendation
Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval
Improve collaborative filtering through bordered block diagonal form matrices
Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval
A HITS-based POI recommendation algorithm for location-based social networks
Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
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
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
Where you like to go next: successive point-of-interest recommendation
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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GPS data tracked on mobile devices contains rich information about human activities and preferences. In this paper, GPS data is used in location-based services (LBSs) to provide collaborative location recommendations. We observe that most existing LBSs provide location recommendations by clustering the User-Location matrix. Since the User-Location matrix created based on GPS data is huge, there are two major problems with these methods. First, the number of similar locations that need to be considered in computing the recommendations can be numerous. As a result, the identification of truly relevant locations from numerous candidates is challenging. Second, the clustering process on large matrix is time consuming. Thus, when new GPS data arrives, complete re-clustering of the whole matrix is infeasible. To tackle these two problems, we propose the Collaborative Location Recommendation (CLR) framework for location recommendation. By considering activities (i.e., temporal preferences) and different user classes (i.e., Pattern Users, Normal Users, and Travelers) in the recommendation process, CLR is capable of generating more precise and refined recommendations to the users compared to the existing methods. Moreover, CLR employs a dynamic clustering algorithm CADC to cluster the trajectory data into groups of similar users, similar activities and similar locations efficiently by supporting incremental update of the groups when new GPS trajectory data arrives. We evaluate CLR with a real-world GPS dataset, and confirm that the CLR framework provides more accurate location recommendations compared to the existing methods.