publication . Conference object . Preprint . Other literature type . 2016

Learning Points and Routes to Recommend Trajectories

Dawei Chen; Cheng Soon Ong; Lexing Xie;
Open Access
  • Published: 25 Aug 2016
  • Publisher: ACM
Abstract
The problem of recommending tours to travellers is an important and broadly studied area. Suggested solutions include various approaches of points-of-interest (POI) recommendation and route planning. We consider the task of recommending a sequence of POIs, that simultaneously uses information about POIs and routes. Our approach unifies the treatment of various sources of information by representing them as features in machine learning algorithms, enabling us to learn from past behaviour. Information about POIs are used to learn a POI ranking model that accounts for the start and end points of tours. Data about previous trajectories are used for learning transiti...
Persistent Identifiers
Subjects
free text keywords: Computer Science - Learning, Computer Science - Information Retrieval, Artificial intelligence, business.industry, business, F1 score, Learning to rank, Route planning, Computer science, Trajectory, Ranking, Statistical model, Machine learning, computer.software_genre, computer
29 references, page 1 of 2

[1] J. Bao, Y. Zheng, D. Wilkie, and M. Mokbel. Recommendations in location-based social networks: a survey. GeoInformatica, 19(3):525{565, 2015.

[2] R. Baraglia, C. I. Muntean, F. M. Nardini, and F. Silvestri. LearNext: learning to predict tourists movements. CIKM '13, pages 751{756. ACM, 2013.

[3] C. Chen, D. Zhang, B. Guo, X. Ma, G. Pan, and Z. Wu. TripPlanner: Personalized trip planning lever-

[4] C. Cheng, H. Yang, M. R. Lyu, and I. King. Where you like to go next: Successive point-of-interest recommendation. IJCAI '13, pages 2605{2611. AAAI Press, 2013.

[5] M. De Choudhury, M. Feldman, S. Amer-Yahia, N. Golbandi, R. Lempel, and C. Yu. Automatic construction of travel itineraries using social breadcrumbs. HT '10, pages 35{44. ACM, 2010.

[6] Flickr. Flickr photos on the map. https://www. ickr. com/map , retrieved May 2016.

[7] Foursquare. FourSquare: about us. https://foursquare. com/about , retrieved May 2016.

[8] H. Gao, J. Tang, X. Hu, and H. Liu. Exploring temporal e ects for location recommendation on location-based social networks. RecSys '13, pages 93{100. ACM, 2013.

[9] A. Gionis, T. Lappas, K. Pelechrinis, and E. Terzi. Customized tour recommendations in urban areas. WSDM '14, pages 313{322. ACM, 2014. [OpenAIRE]

[10] Gurobi. Gurobi Optimization. http://www.gurobi.com , retrieved May 2016.

[11] H.-P. Hsieh and C.-T. Li. Mining and planning timeaware routes from check-in data. CIKM '14, pages 481{ 490. ACM, 2014.

[12] C.-P. Lee and C.-b. Lin. Large-scale linear rankSVM. Neural computation, 26(4):781{817, 2014.

[13] D. Lian, C. Zhao, X. Xie, G. Sun, E. Chen, and Y. Rui. GeoMF: Joint geographical modeling and matrix factorization for point-of-interest recommendation. KDD '14, pages 831{840. ACM, 2014.

[14] K. H. Lim, J. Chan, C. Leckie, and S. Karunasekera. Personalized tour recommendation based on user interests and points of interest visit durations. IJCAI '15, 2015.

[15] Q. Liu, S. Wu, L. Wang, and T. Tan. Predicting the next location: A recurrent model with spatial and temporal contexts. AAAI '16, 2016.

29 references, page 1 of 2
Abstract
The problem of recommending tours to travellers is an important and broadly studied area. Suggested solutions include various approaches of points-of-interest (POI) recommendation and route planning. We consider the task of recommending a sequence of POIs, that simultaneously uses information about POIs and routes. Our approach unifies the treatment of various sources of information by representing them as features in machine learning algorithms, enabling us to learn from past behaviour. Information about POIs are used to learn a POI ranking model that accounts for the start and end points of tours. Data about previous trajectories are used for learning transiti...
Persistent Identifiers
Subjects
free text keywords: Computer Science - Learning, Computer Science - Information Retrieval, Artificial intelligence, business.industry, business, F1 score, Learning to rank, Route planning, Computer science, Trajectory, Ranking, Statistical model, Machine learning, computer.software_genre, computer
29 references, page 1 of 2

[1] J. Bao, Y. Zheng, D. Wilkie, and M. Mokbel. Recommendations in location-based social networks: a survey. GeoInformatica, 19(3):525{565, 2015.

[2] R. Baraglia, C. I. Muntean, F. M. Nardini, and F. Silvestri. LearNext: learning to predict tourists movements. CIKM '13, pages 751{756. ACM, 2013.

[3] C. Chen, D. Zhang, B. Guo, X. Ma, G. Pan, and Z. Wu. TripPlanner: Personalized trip planning lever-

[4] C. Cheng, H. Yang, M. R. Lyu, and I. King. Where you like to go next: Successive point-of-interest recommendation. IJCAI '13, pages 2605{2611. AAAI Press, 2013.

[5] M. De Choudhury, M. Feldman, S. Amer-Yahia, N. Golbandi, R. Lempel, and C. Yu. Automatic construction of travel itineraries using social breadcrumbs. HT '10, pages 35{44. ACM, 2010.

[6] Flickr. Flickr photos on the map. https://www. ickr. com/map , retrieved May 2016.

[7] Foursquare. FourSquare: about us. https://foursquare. com/about , retrieved May 2016.

[8] H. Gao, J. Tang, X. Hu, and H. Liu. Exploring temporal e ects for location recommendation on location-based social networks. RecSys '13, pages 93{100. ACM, 2013.

[9] A. Gionis, T. Lappas, K. Pelechrinis, and E. Terzi. Customized tour recommendations in urban areas. WSDM '14, pages 313{322. ACM, 2014. [OpenAIRE]

[10] Gurobi. Gurobi Optimization. http://www.gurobi.com , retrieved May 2016.

[11] H.-P. Hsieh and C.-T. Li. Mining and planning timeaware routes from check-in data. CIKM '14, pages 481{ 490. ACM, 2014.

[12] C.-P. Lee and C.-b. Lin. Large-scale linear rankSVM. Neural computation, 26(4):781{817, 2014.

[13] D. Lian, C. Zhao, X. Xie, G. Sun, E. Chen, and Y. Rui. GeoMF: Joint geographical modeling and matrix factorization for point-of-interest recommendation. KDD '14, pages 831{840. ACM, 2014.

[14] K. H. Lim, J. Chan, C. Leckie, and S. Karunasekera. Personalized tour recommendation based on user interests and points of interest visit durations. IJCAI '15, 2015.

[15] Q. Liu, S. Wu, L. Wang, and T. Tan. Predicting the next location: A recurrent model with spatial and temporal contexts. AAAI '16, 2016.

29 references, page 1 of 2
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