Skip to main content
Top

2015 | OriginalPaper | Chapter

7. Recommending Hotels by Social Conditions of Locations

Authors : Mohammad Shamsul Arefin, Zhichao Chang, Yasuhiko Morimoto

Published in: Tourism Informatics

Publisher: Springer Berlin Heidelberg

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

In the field of information technology, a recommendation system is a computer program that provides valuable information for the users and guides them to take efficient decisions. The recommendation systems play a vital role in reducing time and effort of users to choose their desired products/services. With rapid growth of Internet technologies recommender systems become very popular to the users nowadays. In this paper, we present a system for recommending hotels for the users. Conventional hotel recommendation systems recommend hotels based on non-spatial attributes of hotels such as price and rating and do not utilize their social locations well. In contrast, proposed system considers the co-existence of other facilities such as restaurants and entertainment facilities in the surrounding areas while selecting a hotel for recommendation. We first evaluate the social conditions of each hotel. Then, we consider user provided reviews about hotels where he stayed earlier. Based on the user’s review, we calculate preferences of that user. Finally, we calculate similarity score between the hotels and the user’s preferences and select the top-k hotels. We perform different experiments to show the effectiveness of our approach. Experimental evaluation shows that our approach is well applicable for recommending hotels for the users.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Literature
5.
go back to reference Adomavicius, G., Tuzhilin, A.: Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions. IEEE Trans. Knowl. Data Eng. 17, 734–749 (2005)CrossRef Adomavicius, G., Tuzhilin, A.: Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions. IEEE Trans. Knowl. Data Eng. 17, 734–749 (2005)CrossRef
6.
go back to reference Pazzani, M.J., Billsus, D.: Content-Based Recommendation Systems. LNCS, vol. 4321, pp. 325–341 (2007) Pazzani, M.J., Billsus, D.: Content-Based Recommendation Systems. LNCS, vol. 4321, pp. 325–341 (2007)
7.
go back to reference Debnath, S., Ganguly, N., Mitra, P.: Feature weighting in content based recommendation system using social network analysis. In: Proceedings of WWW 2008, pp. 1041–1042 (2008) Debnath, S., Ganguly, N., Mitra, P.: Feature weighting in content based recommendation system using social network analysis. In: Proceedings of WWW 2008, pp. 1041–1042 (2008)
8.
go back to reference Horozov, T., Narasimhan, N.: Using location for personalized POI recommendations in mobile environments. In: Proceedings of International Symposium on Applications and the Internet, pp. 124–129 (2006) Horozov, T., Narasimhan, N.: Using location for personalized POI recommendations in mobile environments. In: Proceedings of International Symposium on Applications and the Internet, pp. 124–129 (2006)
9.
go back to reference Kodama, K., Iijima, Y., Guo, X., Ishikawa, Y.: Skyline queries based on user locations and preferences for making location-based recommendations. In: Proceedings of LBSN, pp. 9–16 (2009) Kodama, K., Iijima, Y., Guo, X., Ishikawa, Y.: Skyline queries based on user locations and preferences for making location-based recommendations. In: Proceedings of LBSN, pp. 9–16 (2009)
10.
go back to reference Ye, M., Yin, P., Lee, W. C., Lee, D. L.: Exploiting geographical influence for collaborative point-of-interest recommendation. In: Proceedings of SIGIR, pp. 325–334 (2011) Ye, M., Yin, P., Lee, W. C., Lee, D. L.: Exploiting geographical influence for collaborative point-of-interest recommendation. In: Proceedings of SIGIR, pp. 325–334 (2011)
11.
go back to reference Park, M.H., Hong, J.H., Cho, S.B.: Location-Based Recommendation System using Bayesian User’s Preference Model in Mobile Devices. LNCS, vol. 4611, pp. 1130–1139 (2007) Park, M.H., Hong, J.H., Cho, S.B.: Location-Based Recommendation System using Bayesian User’s Preference Model in Mobile Devices. LNCS, vol. 4611, pp. 1130–1139 (2007)
12.
go back to reference Takeuchi, Y., Sugimoto, M.: CityVoyager: an Outdoor Recommendation System Based on User Location History. LNCS, vol. 4159, pp. 625–636 (2006) Takeuchi, Y., Sugimoto, M.: CityVoyager: an Outdoor Recommendation System Based on User Location History. LNCS, vol. 4159, pp. 625–636 (2006)
13.
go back to reference Zheng, V.W., Zheng, Y., Xie, X., Yang, Q.: Collaborative location and activity recommendations with GPS history data. In: Proceedings of the 19th International Conference on World Wide Web, pp. 1029–1038 (2010) Zheng, V.W., Zheng, Y., Xie, X., Yang, Q.: Collaborative location and activity recommendations with GPS history data. In: Proceedings of the 19th International Conference on World Wide Web, pp. 1029–1038 (2010)
14.
go back to reference Zheng, Y., Xie, X.: Learning travel recommendation from user-generated GPS trajectories. ACM Trans. Intell. Syst. Technol. 2, 1–2 (2011)CrossRef Zheng, Y., Xie, X.: Learning travel recommendation from user-generated GPS trajectories. ACM Trans. Intell. Syst. Technol. 2, 1–2 (2011)CrossRef
15.
go back to reference Zheng, Y., Xie, X., Ma, W.: GeoLife: a collaborative social networking service among user location and trajectory. IEEE Database Eng. Bull. 33, 32–40 (2010) Zheng, Y., Xie, X., Ma, W.: GeoLife: a collaborative social networking service among user location and trajectory. IEEE Database Eng. Bull. 33, 32–40 (2010)
16.
go back to reference Zheng, Y., Zhang, L., Ma, Z., Xie, X., Ma, W.: Recommending friends and locations based on individual location history. ACM Trans. Web 5, 1–44 (2011)CrossRefMATH Zheng, Y., Zhang, L., Ma, Z., Xie, X., Ma, W.: Recommending friends and locations based on individual location history. ACM Trans. Web 5, 1–44 (2011)CrossRefMATH
Metadata
Title
Recommending Hotels by Social Conditions of Locations
Authors
Mohammad Shamsul Arefin
Zhichao Chang
Yasuhiko Morimoto
Copyright Year
2015
Publisher
Springer Berlin Heidelberg
DOI
https://doi.org/10.1007/978-3-662-47227-9_7

Premium Partner