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Generic POI recommendation

Published: 12 September 2020 Publication History

Abstract

For avoiding excessive congestion of tourists that causes overtourism, we propose a Generic Point of Interest (POI), which is an alternative sightseeing spot potentially attractive enough for tourists to replace a well-known sightseeing spot. We also propose a method to discover generic POIs and evaluate it. While the rapid spread of social networking services (SNSs) and social media makes tourism more familiar to people, it is further aggravating overtoursim around the world due to the nature of SNSs and social media, where users simultaneously find the same posts or articles recommending specific tourist spots and are attracted to the same destinations at the same time. As overtourism has severe influences on both visitors and local residents, it is essential to solve this problem. Although there are many studies providing ways of recommending less crowded tourist spots or mining less-known spots in a famous sightseeing area, we cannot apply those methods as a fundamental solution for overtourism for two reasons: 1) in many cases, the number of tourists already exceeds the touring area's total capacity; and 2) many approaches relying on a number of user-generated data points cannot discover unbusy sightseeing spots since users hardly post reviews nor images. To address these challenges, we propose a novel concept of generic POIs, alternative sightseeing spots to famous spots, and we propose a method to discover generic POIs, whose images are similar to those of existing famous sightseeing spots. We also evaluate our method with collected examples of generic POIs. We hope that the proposed method will help alleviate the overtourism problem in the real world as a first step.

References

[1]
María-del-Mar Alonso-Almeida, Fernando Borrajo-Millán, Liu Yi, et al. 2019. Are social media data pushing overtourism? The case of Barcelona and Chinese tourists. Sustainability 11, 12 (2019), 3356.
[2]
Mike Duignan. 2019. 'Overtourism'? Understanding and Managing Urban Tourism Growth beyond Perceptions: Cambridge Case Study: Strategies and Tactics to Tackle Overtourism. In 'Overtourism'? Understanding and Managing Urban Tourism Growth beyond Perceptions: Case Studies. United Nations World Tourism Organisation (UNWTO), 34--39.
[3]
Masato Hidaka, Yuki Kanaya, Shogo Kawanaka, Yuki Matsuda, Yugo Nakamura, Hirohiko Suwa, Manato Fujimoto, Yutaka Arakawa, and Keiichi Yasumoto. 2020. On-site Trip Planning Support System Based on Dynamic Information on Tourism Spots. Smart Cities 3, 2 (2020), 212--231.
[4]
TJGMJ Mainil, E Eijgelaar, J Klijs, J Nawijn, and PM Peeters. 2017. Research for TRAN committee-health tourism in the EU: a general investigation. (2017).
[5]
Aude Oliva and Antonio Torralba. 2001. Modeling the shape of the scene: A holistic representation of the spatial envelope. International journal of computer vision 42, 3 (2001), 145--175.
[6]
Carol Peters, Thomas Deselaers, Nicola Ferro, Julio Gonzalo, Gareth JF Jones, Mikko Kurimo, Thomas Mandl, Anselmo Penas, and Vivien Petras. 2008. Evaluating Systems for Multilingual and Multimodal Information Access 9th Workshop of the Cross-Language Evaluation Forum, CLEF 2008, Aarhus, Denmark, September 17--19, 2008, Revised Selected Papers. In Conference proceedings CLEF. Springer, 527.
[7]
T. Yamashita and Koichi Kurumatani. 2009. New approach to smooth traffic flow with route information sharing. Multi-Agent Systems for Traffic and Transportation Engineering (01 2009), 291--306.
[8]
Bolei Zhou, Agata Lapedriza, Aditya Khosla, Aude Oliva, and Antonio Torralba. 2017. Places: A 10 million image database for scene recognition. IEEE transactions on pattern analysis and machine intelligence 40, 6 (2017), 1452--1464.
[9]
Chenyi Zhuang, Qiang Ma, Xuefeng Liang, and Masatoshi Yoshikawa. 2014. Anaba: An obscure sightseeing spots discovering system. In 2014 IEEE International Conference on Multimedia and Expo (ICME). IEEE, 1--6.

Cited By

View all
  • (2024)Urban Overtourism Detection Based on Graph Temporal Convolutional NetworksIEEE Transactions on Computational Social Systems10.1109/TCSS.2022.322617711:1(442-454)Online publication date: Feb-2024
  • (2023)Characterizing Generic POI: A Novel Approach for Discovering Tourist AttractionsJournal of Information Processing10.2197/ipsjjip.31.26531(265-277)Online publication date: 2023
  • (2022)Complementing Location-Based Social Network Data With Mobility Data: A Pattern-Based ApproachIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2022.318256923:11(21216-21227)Online publication date: Nov-2022
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Information & Contributors

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Published In

cover image ACM Conferences
UbiComp/ISWC '20 Adjunct: Adjunct Proceedings of the 2020 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2020 ACM International Symposium on Wearable Computers
September 2020
732 pages
ISBN:9781450380768
DOI:10.1145/3410530
Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 12 September 2020

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Author Tags

  1. POI
  2. sightseeing spot recommendation
  3. social media photographs

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UbiComp/ISWC '20

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Overall Acceptance Rate 764 of 2,912 submissions, 26%

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Cited By

View all
  • (2024)Urban Overtourism Detection Based on Graph Temporal Convolutional NetworksIEEE Transactions on Computational Social Systems10.1109/TCSS.2022.322617711:1(442-454)Online publication date: Feb-2024
  • (2023)Characterizing Generic POI: A Novel Approach for Discovering Tourist AttractionsJournal of Information Processing10.2197/ipsjjip.31.26531(265-277)Online publication date: 2023
  • (2022)Complementing Location-Based Social Network Data With Mobility Data: A Pattern-Based ApproachIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2022.318256923:11(21216-21227)Online publication date: Nov-2022
  • (2022)Tourism recommendation system: a survey and future research directionsMultimedia Tools and Applications10.1007/s11042-022-12167-w82:6(8983-9027)Online publication date: 21-Apr-2022
  • (2021)Systematic Review of Contextual Suggestion and Recommendation Systems for Sustainable e-TourismSustainability10.3390/su1315814113:15(8141)Online publication date: 21-Jul-2021

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