TripRec: recommending trip routes from large scale check-in data
Proceedings of the 21st international conference companion on World Wide Web
Keyword-aware optimal route search
Proceedings of the VLDB Endowment
Personalized trip recommendation with multiple constraints by mining user check-in behaviors
Proceedings of the 20th International Conference on Advances in Geographic Information Systems
Mining interesting user behavior patterns in mobile commerce environments
Applied Intelligence
Where shall we go today?: planning touristic tours with tripbuilder
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
TripCloud: an intelligent cloud-based trip recommendation system
SSTD'13 Proceedings of the 13th international conference on Advances in Spatial and Temporal Databases
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With the rapid development of wireless telecommunication technologies, a number of studies have been done on the Location-Based Services (LBSs) due to wide applications. Among them, one of the active topics is travel recommendation. Most of previous studies focused on recommendations of attractions or trips based on the user隆娄s location. However, such recommendation results may not satisfy the travel time constraints of users. Besides, the efficiency of trip planning is sensitive to the scalability of travel regions. In this paper, we propose a novel data mining-based approach, namely Trip-Mine, to efficiently find the optimal trip which satisfies the user隆娄s travel time constraint based on the user隆娄s location. Furthermore, we propose three optimization mechanisms based on Trip-Mine to further enhance the mining efficiency and memory storage requirement for optimal trip finding. To the best of our knowledge, this is the first work that takes efficient trip planning and travel time constraints into account simultaneously. Finally, we performed extensive experimental evaluations and show that our proposals deliver excellent results.