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Location-Based Hotel Recommendation System

  • Chien-Liang ChenEmail author
  • Ching-Sheng Wang
  • Ding-Jung Chiang
Conference paper
Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 264)

Abstract

In recent years, the hotel industry in Taiwan has begun to flourish as the economy has grown. In order to attract more tourists to make changes in various services and facilities, the hotel’s types have begun to make a difference. However, the content of the website is full of personal subjective or unilateral information, which is easy for tourists to lose in it or waste a lot of time cost. Therefore, we hope to provide more comprehensive hotel recommendations and use the traditional recommendation technology combined with location-based services to make recommendations. Different from the conventional recommendation, only comprehensive factors are considered. The study included three individual factors – service, price, facility to do a single rating and combined with the location of the tourist to make recommendations so that the recommendations can be closer to the needs of tourists. We selected 50 high-profile hotels, including five categories of mountain, sea, hot springs, theme parks, and resort hotels. Through the recommendation system, we recommend hotels that have not yet been lived by tourists, as a list of hotels to choose from it.

Keywords

Location-based services Recommendation system 

References

  1. 1.
    O’Mahony, M.P., Smyth, B.: A classification-based review recommender. In: Bramer, M., Ellis, R., Petridis, M. (eds.), Research and Development in Intelligent Systems XXVI, pp. 49–62 (2010)Google Scholar
  2. 2.
    Ben Schafer, J., Konstan, J.A., Riedl, J.: E-commerce recommendation applications. Data Min. Knowl. Discov. 5(1), 115–153 (2001)Google Scholar
  3. 3.
    Belkin, N.J., Bruce Croft, W.: Information filtering and information retrieval: two sides of the same coin? Commun. ACM 35(12), 29–38 (1992)Google Scholar
  4. 4.
    Mooney, R.J., Roy, L.: Content-based book recommending using learning for text categorization. In: Proceedings of the Fifth ACM Conference on Digital Libraries, DL 2000, pp. 195–204 (2000)Google Scholar
  5. 5.
    Goldberg, D., Nichols, D., Oki, B.M., Terry, D.: Using collaborative filtering to weave an information tapestry. Commun. ACM 35(12), 61–70 (1992)CrossRefGoogle Scholar
  6. 6.
    Resnick, P., Iacovou, N., Suchak, M., Bergstrom, P., Riedl, J.: Grouplens: an open architecture for collaborative filtering of netnews. In: Proceedings of the 1994 ACM Conference on Computer Supported Cooperative Work, pp. 175–186 (1994)Google Scholar
  7. 7.
    Dhillon, N.: Achieving effective personalization and customization using collaborative filtering. http://home1.gte.net/dhillos/cf. October 1995
  8. 8.
    Kim, B.M., Li, Q., Park, C.S., Kim, S.G., Kim, J.Y.: A new approach for combining content-based and collaborative filters. J. Intell. Inf. Syst. 27(1), 79–91 (2006)Google Scholar
  9. 9.
    Ahmad Wasfi, A.M.: Collecting user access patterns for building user profiles and collaborative filtering. In: Proceedings of the 4th International Conference on Intelligent User Interfaces, IUI 1999, pp. 57–64 (1999)Google Scholar
  10. 10.
    Claypool, M., Gokhale, A., Miranda, T., Murnikov, P., Netes, D., Sartin, M.: Combining content-based and collaborative filters in an online newspaper (1999)Google Scholar
  11. 11.
    Balabanović, M., Shoham, Y.: Fab: content-based, collaborative recommendation. Commun. ACM 40(3), 66–72 (1997)Google Scholar
  12. 12.
    Sarwar, B., Karypis, G., Konstan, J., Riedl, J.: Item-based collaborative filtering recommendation algorithms. In: Proceedings of the 10th International Conference on World Wide Web, pp. 285–295 (2001)Google Scholar
  13. 13.
    Montaner, M., L´opez, B., De La Rosa, J.L.: A taxonomy of recommender agents on theinternet. Artif. Intell. Rev. 19(4), 285–330 (2003)Google Scholar
  14. 14.
    Porter, M.E.: Strategy and the internet. Harv. Bus. Rev. 79(164), 62–78 (2001)Google Scholar
  15. 15.
    Sarwar, B.M., Konstan, J.A., Borchers, A., Herlocker, J., Miller, B., Riedl, J.: Using filtering agents to improve prediction quality in the grouplens research collaborative filtering system. In: Proceedings of the 1998 ACM Conference on Computer Supported Cooperative Work, pp. 345–354 (1998)Google Scholar

Copyright information

© ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2019

Authors and Affiliations

  • Chien-Liang Chen
    • 1
    Email author
  • Ching-Sheng Wang
    • 1
  • Ding-Jung Chiang
    • 2
  1. 1.Department of Computer Science and Information EngineeringAletheia UniversityNew Taipei CityTaiwan
  2. 2.Department of Digital Multimedia DesignTaipei Chengshih University of Science and TechnologyTaipeiTaiwan

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