Abstract
Route recommendation based on users’ historical trajectories and behavior preferences is one of the important research problems. However, most of the existing work recommends a route based on the similarity among the routes in historical trajectories. As a result, hidden routes that also meet the users’ requirements cannot be explored. To solve this problem, we developed a system PHR that can recommend hidden routes to users employing the Hidden Markov Model, where a route recommendation problem is transformed to a point-of-interested (POI) sequence prediction. The system can return the top-k results including both explicit and hidden routes considering the personalized category sequence, route length, POI popularity, and visiting probabilities. The real check-in data from Foursquare is employed in this demo. The research can be used for travel itinerary plan or routine trip plan.
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References
Pan, X., Yang, Y.D., Yao, X., et al.: Personalized hidden route recommendation based on Hidden Markov Model. J. Zhejiang Univ. (Eng. Sci.) 54(9), 1736–1745 (2020). (in Chinese)
Qiao, S.J., Shen, D.Y., et al.: A self-adaptive parameter selection trajectory prediction approach via Hidden Markov Models. IEEE Trans. Intell. Transp. Syst. 16(1), 284–296 (2015)
Jie, B., Yu, Z., et al.: Location-based and preference-aware recommendation using sparse geo-social networking data. In: Proceedings of ACM GIS, pp. 199–208 (2012)
Wei, L.Y., Zheng, Y., Peng, W.: Constructing popular routes from uncertain trajectories. In: Proceedings of SIGKDD Annual Conference of ACM, pp. 195–203 (2012)
Chen, D.W., Ong, C.S., et al.: Learning points and routes to recommend trajectories. In: Author Proof Proceedings of CIKM, pp. 2227–2232 (2016)
Acknowledgment
This research was partially supported by the Natural Science Foundation of Hebei Province (F2018210109), the Key projects from the Hebei Education Department (No. ZD2018040), the Foundation of Introduction of Oversea Scholar (C201822), the Basic Research Team Project from Science and Technology Department (2019JT70803), the Fourth Outstanding Youth Foundation of Shijiazhuang Tiedao University.
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Yang, Y., Pan, X., Yao, X., Wang, S., Han, L. (2020). PHR: A Personalized Hidden Route Recommendation System Based on Hidden Markov Model. In: Wang, X., Zhang, R., Lee, YK., Sun, L., Moon, YS. (eds) Web and Big Data. APWeb-WAIM 2020. Lecture Notes in Computer Science(), vol 12318. Springer, Cham. https://doi.org/10.1007/978-3-030-60290-1_42
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DOI: https://doi.org/10.1007/978-3-030-60290-1_42
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