skip to main content
10.1145/3477314.3507244acmconferencesArticle/Chapter ViewAbstractPublication PagessacConference Proceedingsconference-collections
poster

A diversity personalization approach towards recommending POIs for Jeju island

Published:06 May 2022Publication History

ABSTRACT

In this paper, we propose a diversity personalization approach: we find out how important each user considers diversity of a travel destination when choosing where to visit. Then, we provide recommendations on tourist attractions by combining the score of the personalized diversity, predicted rating score and popularity of POI. We crawled TripAdvisor and Naver data to evaluate the proposed method. Experimental results show that the proposed method shows meaningful improvements in Recall, nDCG, and MRR in terms of top-1, top-2, and top-3 recommendations compared to several baselines.

References

  1. Dong-Kyu Chae, Jin-Soo Kang, Sang-Wook Kim, and Jung-Tae Lee. 2018. Cfgan: A generic collaborative filtering framework based on generative adversarial networks. In Proceedings of the 27th ACM international conference on information and knowledge management. 137--146.Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. Dong-Kyu Chae, Sang-Wook Kim, and Jung-Tae Lee. 2019. Autoencoder-based personalized ranking framework unifying explicit and implicit feedback for accurate top-N recommendation. Knowledge-Based Systems 176 (2019), 110--121.Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. Dawei Chen, Cheng Soon Ong, and Lexing Xie. 2016. Learning points and routes to recommend trajectories. In Proceedings of the 25th ACM International on Conference on Information and Knowledge Management. 2227--2232.Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. Yong Ge, Qi Liu, Hui Xiong, Alexander Tuzhilin, and Jian Chen. 2011. Cost-aware travel tour recommendation. In Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining. 983--991.Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. Jiayuan He, Jianzhong Qi, and Kotagiri Ramamohanarao. 2019. A joint context-aware embedding for trip recommendations. In 2019 IEEE 35th International Conference on Data Engineering (ICDE). IEEE, 292--303.Google ScholarGoogle ScholarCross RefCross Ref
  6. Oleksii Kuchaiev and Boris Ginsburg. 2017. Training deep autoencoders for collaborative filtering. arXiv preprint arXiv:1708.01715 (2017).Google ScholarGoogle Scholar
  7. Petro Liashchynskyi and Pavlo Liashchynskyi. 2019. Grid search, random search, genetic algorithm: a big comparison for NAS. arXiv preprint arXiv:1912.06059 (2019).Google ScholarGoogle Scholar
  8. Kwan Hui Lim. 2015. Recommending tours and places-of-interest based on user interests from geo-tagged photos. In Proceedings of the 2015 ACM SIGMOD on PhD Symposium. 33--38.Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. Kwan Hui Lim, Jeffrey Chan, Shanika Karunasekera, and Christopher Leckie. 2017. Personalized itinerary recommendation with queuing time awareness. In Proceedings of the 40th international ACM SIGIR conference on research and development in information retrieval. 325--334.Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. Kwan Hui Lim, Jeffrey Chan, Christopher Leckie, and Shanika Karunasekera. 2015. Personalized tour recommendation based on user interests and points of interest visit durations. In Twenty-Fourth International Joint Conference on Artificial Intelligence.Google ScholarGoogle Scholar
  11. Shintaro Okazaki, Luisa Andreu, and Sara Campo. 2017. Knowledge sharing among tourists via social media: A comparison between Facebook and TripAdvisor. International Journal of Tourism Research 19, 1 (2017), 107--119.Google ScholarGoogle ScholarCross RefCross Ref
  12. Paul Resnick, Neophytos Iacovou, Mitesh Suchak, Peter Bergstrom, and John Riedl. 1994. Grouplens: An open architecture for collaborative filtering of netnews. In Proceedings of the 1994 ACM conference on Computer supported cooperative work. 175--186.Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. Francesco Ricci, Lior Rokach, and Bracha Shapira. 2011. Introduction to recommender systems handbook. In Recommender systems handbook. Springer, 1--35.Google ScholarGoogle Scholar
  14. Suvash Sedhain, Aditya Krishna Menon, Scott Sanner, and Lexing Xie. 2015. Autorec: Autoencoders meet collaborative filtering. In Proceedings of the 24th international conference on World Wide Web. 111--112.Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. Tourism Organisation UNWTO. 2020. Tourism Highlights, 2019 edition. World (2020).Google ScholarGoogle Scholar
  16. Hao Wang, Naiyan Wang, and Dit-Yan Yeung. 2015. Collaborative deep learning for recommender systems. In Proceedings of the 21th ACM SIGKDD international conference on knowledge discovery and data mining. 1235--1244.Google ScholarGoogle ScholarDigital LibraryDigital Library
  17. Zheng Xiang, Dan Wang, Joseph T O'Leary, and Daniel R Fesenmaier. 2015. Adapting to the internet: trends in travelers' use of the web for trip planning. Journal of travel research 54, 4 (2015), 511--527.Google ScholarGoogle ScholarCross RefCross Ref
  18. Alexandre Yahi, Antoine Chassang, Louis Raynaud, Hugo Duthil, and Duen Horng Chau. 2015. Aurigo: an interactive tour planner for personalized itineraries. In Proceedings of the 20th international conference on intelligent user interfaces. 275--285.Google ScholarGoogle ScholarDigital LibraryDigital Library

Index Terms

  1. A diversity personalization approach towards recommending POIs for Jeju island

        Recommendations

        Comments

        Login options

        Check if you have access through your login credentials or your institution to get full access on this article.

        Sign in
        • Published in

          cover image ACM Conferences
          SAC '22: Proceedings of the 37th ACM/SIGAPP Symposium on Applied Computing
          April 2022
          2099 pages
          ISBN:9781450387132
          DOI:10.1145/3477314

          Copyright © 2022 Owner/Author

          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.

          Publisher

          Association for Computing Machinery

          New York, NY, United States

          Publication History

          • Published: 6 May 2022

          Check for updates

          Qualifiers

          • poster

          Acceptance Rates

          Overall Acceptance Rate1,650of6,669submissions,25%

        PDF Format

        View or Download as a PDF file.

        PDF

        eReader

        View online with eReader.

        eReader