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International Conference on Smart City and Informatization

iSCI 2019: Smart City and Informatization pp 197–210Cite as

Context-Aware Personalized POI Sequence Recommendation

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Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1122))

Abstract

The Point Of Interest (POI) sequence recommendation applies to scenarios like itinerary and travel route planning which belongs to the class of NP-hard problem. What’s more, the external environment like the weather, time can affect the user’s check-in behavior such as people prefer to check-in in ice cream shop when the temperature is higher. We propose an algorithm to solve these problems that called Context-Aware Personalized POI sequence Recommendation based on reinforcement learning (CAPR for short). First, we model the users dynamic preferences that incorporate contextual information associated with the users’ sequence of check-ins. Then we use the Monte Carlo Tree Search algorithm to select the user-satisfied POI in different environments. What’s more, we can get the context-aware personalized POI sequence under the specified time limit. Finally, we test the proposed algorithm using an open source dataset. The experimental results show that the weather and time context can improve the accuracy of the recommendation. Our algorithm can improve the effectiveness of travel recommendations.

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Notes

  1. 1.

    https://www.yongliu.org/datasets/.

  2. 2.

    https://api.darksky.net/forecast/.

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Acknowledgments

This research was supported by NSFC grant 61632009 and Outstanding Young Talents Training Program in Hunan University 531118040173.

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Authors and Affiliations

  1. College of Computer Science and Electronic Engineering, Hunan University, Changsha, 410082, China

    Jing Chen & Wenjun Jiang

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  1. Jing Chen
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  2. Wenjun Jiang
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Correspondence to Wenjun Jiang .

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Editors and Affiliations

  1. Guangzhou University, Guangzhou, China

    Guojun Wang

  2. University of Ottawa, Ottawa, ON, Canada

    Abdulmotaleb El Saddik

  3. Shanghai Jiao Tong University, Shanghai, China

    Xuejia Lai

  4. University of Murcia, Murcia, Spain

    Gregorio Martinez Perez

  5. The University of Texas at San Antonio, San Antonio, TX, USA

    Kim-Kwang Raymond Choo

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Cite this paper

Chen, J., Jiang, W. (2019). Context-Aware Personalized POI Sequence Recommendation. In: Wang, G., El Saddik, A., Lai, X., Martinez Perez, G., Choo, KK. (eds) Smart City and Informatization. iSCI 2019. Communications in Computer and Information Science, vol 1122. Springer, Singapore. https://doi.org/10.1007/978-981-15-1301-5_16

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  • DOI: https://doi.org/10.1007/978-981-15-1301-5_16

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-15-1300-8

  • Online ISBN: 978-981-15-1301-5

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