ABSTRACT
Location-based services allow users to perform check-in actions, which not only record their geo-spatial activities, but also provide a plentiful source for data scientists to analyze and plan more accurate and useful geographical recommender system. In this paper, we present a novel Time-aware Route Planning (TRP) problem using location check-in data. The central idea is that the pleasure of staying at the locations along a route is significantly affected by their visiting time. Each location has its own proper visiting time due to the category, objective, and population. To consider the visiting time of locations into route planning, we develop a three-stage time-aware route planning framework. First, since there is usually either noise time on existing locations or no visiting information on new locations constructed, we devise an inference method, LocTimeInf, to predict and recover the location visiting time on routes. Second, we aim to find the representative and popular time-aware location-transition behaviors from user check-in data, and a Time-aware Transit Pattern Mining (TTPM) algorithm is proposed correspondingly. Third, based on the mined time-aware transit patterns, we develop a Proper Route Search (PR-Search) algorithm to construct the final time-aware routes for recommendation. Experiments on Gowalla check-in data exhibit the promising effectiveness and efficiency of the proposed methods, comparing to a series of competitors.
- R. Agrawal, R. Srikant. Fast algorithms for mining association rules. VLDB 1994. Google ScholarDigital Library
- R. Agrawal, R. Srikant. Mining Sequential Patterns. IEEE ICDE 1995. Google ScholarDigital Library
- J. Ayres, J.E. Gehrke, T. Yiu, J. Flannick. Sequential pattern mining using a bitmap representation. ACM SIGMOD 2002.Google ScholarDigital Library
- Z. Cheng, J. Caverlee, K. Lee, and D. Sui. Exploring Millions of Footprints in Location Sharing Services. ICWSM 2011.Google Scholar
- M.-F. Chiang, Y.-H. Lin, W.-C. Peng, and P. S. Yu. Inferring distant-time location in low-sampling-rate trajectories. ACM KDD 2013. Google ScholarDigital Library
- E. Cho, S. A. Myers, and J. Leskovec. Friendship and mobility: User movement in location-based social networks. ACM KDD 2011. Google ScholarDigital Library
- F. Giannotti, M. Nanni, and D. Pedreschi. Efficient Mining of Temporally Annotated Sequences. SIAM SDM 2006.Google Scholar
- H.-P. Hsieh, C.-T. Li and S.-D. Lin. Measuring and Recommending Time-Sensitive Routes from Location-based Data. ACM TIST 2014. Google ScholarDigital Library
- B. Liu, Y. Fu, Z. Yao, and H. Xiong. Learning Geographical Preferences for Point-of-Interest Recommendation. ACM KDD 2013. Google ScholarDigital Library
- H.-C. Lu, C.-Y. Lin, and V.S. Tseng. Trip-Mine: An Efficient Trip Planning Approach with Travel Time Constraints. IEEE MDM 2011. Google ScholarDigital Library
- X. Lu, C. Wang, J.-M. Yang, Y. Pang, and L. Zang. Photo2trip: Generating Travel Routes from Geo-tagged Photos for Trip Planning. ACM Multimedia 2010. Google ScholarDigital Library
- A. Monreale, F. Pinelli, R. Trasarti, and F. Giannotti. Where next: a location predictor on trajectory pattern mining. ACM KDD 2009. Google ScholarDigital Library
- X. Zhu, Z. Ghahramani and J. Lafferty. Semi-Supervised Learning Using Gaussian Fields and Harmonic Functions. ICML 2003.Google Scholar
- J. Pei, J. Han, B. Mortazavi-Asl, H. Pinto. PrefixSpan: Mining sequential patterns efficiently by prefix-projected pattern growth, IEEE ICDE 2001. Google ScholarDigital Library
- A. Sadilek, H. Kautz, and J. P. Bigham. Finding your friends and following them to where you are. ACM WSDM 2012. Google ScholarDigital Library
- J. Wang, J. Han, C. Li. Frequent closed sequence mining without candidate maintenance. IEEE TKDE 2007. Google ScholarDigital Library
- L.-Y. Wei, Y. Zheng, and W.-C. Peng. Constructing Popular Routes from Uncertain Trajectories. ACM KDD 2012. Google ScholarDigital Library
- X. Yan, J. Han, R. Afshar, CloSpan: Mining closed sequential patterns in large datasets, SIAM SDM 2003.Google ScholarCross Ref
- M. Ye, P. Yin, W.-C. Lee, and D.-L. Lee. Exploiting geographical influence for collaborative point-of-interest recommendation. ACM SIGIR 2011. Google ScholarDigital Library
- M. J. Zaki, SPADE: An efficient algorithm for mining frequent sequences. Machine Learning 2011. Google ScholarDigital Library
- H. Yoon, Y. Zheng, X. Xie, and W. Woo, Social Itinerary Recommendation from User-generated Digital Trails. Personal and Ubiquitous Computing, 2011. Google ScholarDigital Library
- Q. Yuan, G. Cong, Z. Ma, A. Sun, and N. M. Thalmann. Time-aware point-of-interest recommendation. ACM SIGIR 2013. Google ScholarDigital Library
Index Terms
- Mining and Planning Time-aware Routes from Check-in Data
Recommendations
Exploiting large-scale check-in data to recommend time-sensitive routes
UrbComp '12: Proceedings of the ACM SIGKDD International Workshop on Urban ComputingLocation-based services allow users to perform geo-spatial check-in actions, which facilitates the mining of the moving activities of human beings. This paper proposes to recommend time-sensitive trip routes, consisting of a sequence of locations with ...
On route planning by inferring visiting time, modeling user preferences, and mining representative trip patterns
Location-based services allow users to perform check-in actions, which record the geo-spatial activities and provide a plentiful source to do more accurate and useful geographical recommendation. In this paper, we present a novel PreferredTime-aware ...
Inferring visiting time distributions of locations from incomplete check-in data
WWW '14 Companion: Proceedings of the 23rd International Conference on World Wide WebOnline location-based services, such as Foursquare and Facebook, provide a great resource for location recommendation. As we know the time is one of the important factors on recommending places with proper time for users, since the pleasure of visiting ...
Comments