Skip to main content

Advertisement

Springer Nature Link
Log in

Cultural difference and visual information on hotel rating prediction

  • Published:
World Wide Web Aims and scope Submit manuscript

Abstract

Due to the emergence of hotel social media platforms, how to discover interesting properties and utilize these discovered characteristics in hotel-related applications become important issues. In this work, we extend a large-scale hotel information collection to include heterogeneous hotel information, in order to facilitate multimodal and cross-culture analysis. With this rich dataset, we analyze various correlations between hotel properties and unveil interesting characteristics that would benefit hotel recommendation. We found that travelers from different cultural areas (countries) have different rating behaviors. In addition, beyond the scope of conventional text-based hotel analysis, we utilize visual analysis techniques to analyze hotel’s cover photo, and investigate the relationship between rating behaviors and visual information. We adopt these correlations to predict hotel ratings, and verify that by considering visual information and cultural difference, prediction performance can be improved.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
Figure 8
Figure 9
Figure 10
Figure 11
Figure 12
Figure 13
Figure 14
Figure 15
Figure 16

Similar content being viewed by others

Notes

  1. http://www.tripavdisor.com

  2. http://www.agoda.com

  3. http://www.hotels.com

  4. http://www.booking.com

  5. http://www.alexa.com/siteinfo/tripadvisor.com

  6. https://developers.google.com/maps/

References

  1. Abdi, A.: Multiple correlation coefficient. Encyclopedia of Measurement and Statistics, 648–651 (2007)

  2. Banerjee, S., Chua, A.Y.K.: A study of manipulative and authentic negative reviews. In: Proceedings of international conference on ubiquitous information management and communication, p 76 (2014)

    Google Scholar 

  3. Banerjee, S., Chua, A.Y.K.: In Search of Patterns among Travellers’ Hotel Ratings in TripAdvisor. Tour. Manage. 53, 125–131 (2016)

    Article  Google Scholar 

  4. Blei, D.M., Ng. A.Y., Jordan, M.I.: LatentDirichlet Allocation. J. Mach. Learn. Res. 3, 993–1022 (2003)

    MATH  Google Scholar 

  5. Chen, C.-H.: Generalized association plots: Information visualization via iteratively generated correlation matrices. Stat. Sin. 12, 7–29 (2002)

    MathSciNet  MATH  Google Scholar 

  6. Chen, Y.-S., Chen, L.-H., Takama, Y.: Proposal of LDA-based Sentiment Visualization of Hotel Reviews. In: Proceedings of International Conference on Data Mining Workshops, pp. 687–693 (2015)

    Google Scholar 

  7. Chang, C.-C., Lin, C.-J.: LIBSVM: A Library For Support Vector Machines. ACM Trans. Intell. Syst. Technol. 2(3), 27 (2011)

    Article  Google Scholar 

  8. Dai, W., Lin, Q.: Service Design by Utilization of Content Analysis: A Case of Mid-priced Limited Service Hotels in China. In: Proceedings of International Conference on Wireless Communications Networking and Mobile Computing, pp. 13–18 (2014)

    Google Scholar 

  9. Evangelopoulos, G., Zlatintsi, A., Potamianos, A., Maragos, P., Rapantzikos, K., Skoumas, G., Avrithis, Y.: Sentiment Analysis and Opinion Mining. Synthesis Lectures on Human Language Technologies. Morgan & Claypool Publishers (2012)

  10. ESL vocabulary, http://www.manythings.org/vocabulary/lists/a/words.php

  11. Guo, G.: Improving the performance of recommender systems by alleviating the data sparsity and cold start problems. In: Proceedings of International Joint Conference on Artificial Intelligence, pp. 495–496 (2013)

    Google Scholar 

  12. Hargreaves, C.A.: A comparative analysis of hotel ratings and reviews: an application in Singapore. American Journal of Marketing Research 1(3), 118–129 (2015)

    Google Scholar 

  13. Hargreaves, C.A.: Analysis of hotel guest satisfaction ratings and reviews: an application in Singapore. American Journal of Marketing Research 1(4), 208–214 (2015)

    Google Scholar 

  14. Hwang, S.-Y., Lai, C.-Y., Chang, S., Jiang, J.-J.: The identification of noteworthy hotel reviews for hotel management. Pacific Asia Journal of the Association for Information Systems 6(4), 1–17 (2014)

    Google Scholar 

  15. Hariri, N., Zheng, Y., Mobasher, B., Burke, R.: Context-Aware Recommendation based on review mining. In: Proceedings of international workshop on intelligent techniques for Web personalization and recommender systems (2011)

  16. Jiang, Y.-G., Yang, J., Ngo, C.-W., Hauptmann, A.-G.: Representations of Keypoint-Based semantic concept detection: a comprehensive study. IEEE Trans. Multimedia 12(1), 42–53 (2010)

    Article  Google Scholar 

  17. Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet Classification with Deep Convolutional Neural Networks. In: Proceeding of Annual Conference on Neural Information Processing Systems, pp. 1106–1114 (2012)

    Google Scholar 

  18. Lu, Y., Castellanos, M., Dayal, U., Zha, C.-X.: Automatic construction of a Context-Aware sentiment lexicon: an optimization approach. In: Proceedings of International Conference on World Wide Web, pp. 347–356 (2011)

    Google Scholar 

  19. Lin, K.-P., Lai, C.-Y., Chen, P.-C., Hwang, S.-Y.: Personalized hotel recommendation using text mining and mobile browsing tracking. In: Proceedings of IEEE international conference on systems, man, Cybernetics, pp. 191–196 (2015)

    Google Scholar 

  20. Li, H., Lin, R., Hong, R., Ge, Y.: Generative models for mining latent aspects and their ratings from short reviews. In: Proceedings of International Conference on Data Mining, pp. 241–250 (2015)

    Google Scholar 

  21. Liu, B., Zhang, L.: A survey of opinion mining and sentiment analysis. Mining text data, pp. 415–463. Springer (2012)

  22. Minnich, A.J., Chavoshi, N., Mueen, A., Luan, S., Faloutsos, M.: Trueview: Harnessing the Power of Multiple Review Sites. In: Proceedings of International World Wide Web, pp. 787–797 (2015)

    Google Scholar 

  23. Multiple Regression Model, http://www.unesco.org/webworld/idams/advguide/Chapt5.htm

  24. Martin, L., Sintsova, V., Pu, P.: Are influential writers more objective? an analysis of emotionality in review comments. In: Proceedings of International Conference on World Wide Web, pp. 799–804 (2014)

    Google Scholar 

  25. Pazzani, M.J.: A Framework for Collaborative, Content-based and Demographic Filtering. Artif. Intell. Rev. 13(5-6), 393–408 (1999)

    Article  Google Scholar 

  26. Paterek, A.: Improving regularized singular value decomposition for collaborative filtering. In: Proceedings of ACM International Conference on Knowledge Discovery and Data Mining, pp. 39–42 (2007)

    Google Scholar 

  27. Poriya, A., Patel, N., Bhagat, T., Sharma, R.: Non-personalized Recommender Systems and User-based Collaborative Recommender Systems. International Journal of Applied Information Systems 6(9), 22–27 (2014)

    Google Scholar 

  28. Rendle, S.: Factorization machines. In: Proceedings of IEEE International Conference on Data Mining, pp. 995–1000 (2010)

    Google Scholar 

  29. Rianthong, N., Dumrongsiri, A., Kohda, Y.: Improving the multidimensional sequencing of hotel rooms on an online travel agency Web site. Electron. Commer. Res. Appl. 17, 74–86 (2016)

    Article  Google Scholar 

  30. Rahayu, D.A., Krishnaswamy, S., Alahakoon, O., Labbe, C.: Rnr: Extracting Rationale from Online Reviews and Ratings. In: Proceedings of IEEE International Conference on Data Mining Workshops, pp. 358–368 (2010)

    Google Scholar 

  31. Resnick, P., Varian, H.R.: Recommender systems. Commun. ACM 40(3), 56–58 (1997)

    Article  Google Scholar 

  32. Saga, R., Hayashi, Y., Tsuji, H.: Hotel recommender system based on user’s preference transition. In: Proceedings of IEEE international conference on systems Man and Cybernetics, pp. 2437–2442 (2008)

    Google Scholar 

  33. Schafer, J.B., Konstan, J., Riedl, J.: Recommender systems in E-Commerce. In: Proceedings of ACM International Conference on Electronic Commerce, pp. 158–166 (1999)

    Chapter  Google Scholar 

  34. Salakhutdinov, R., Mnih, A.: Probabilistic matrix factorization. In: Proceedings of Annual Conference on Neural Information Processing Systems, pp. 1257–1264 (2007)

    Google Scholar 

  35. Szummer, M., Picard, R.W.: Indoor-Outdoor Image classification. In: Proceedings of IEEE International Workshop on Content-Based Access of Image and Video Database, pp. 42–51 (1998)

    Chapter  Google Scholar 

  36. Traub, M., Kowald, D., Lacic, E., Schoen, P., Supp, G., Lex, E.: Smart Booking without Looking: Providing Hotel Recommendations in the TripRebel Portal. In: Proceedings of International Conference on Knowledge Technologies and Data-driven Business, p 50 (2015)

    Google Scholar 

  37. Wei, X., Luo, X., Li, Q., Zhang, J., Xu, Z.: Online Comment-Based hotel quality automatic assessment using improved fuzzy comprehensive evaluation and fuzzy cognitive map. IEEE Trans. Fuzzy Syst. 23(1), 72–84 (2015)

    Article  Google Scholar 

  38. Wang, H.-N., Lu, Y., Zhai, C.-X.: Latent aspect rating analysis on review text data: a rating regression approach. In: Proceedings of ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pp. 783–792 (2010)

    Google Scholar 

  39. Wang, H.-N., Lu, Y., Zhai, C.-X.: Latent aspect rating analysis without aspect keyword supervision. In: Proceedings of ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pp. 618–626 (2011)

    Google Scholar 

  40. Xiong, Y.-N., Geng, L.-X.: Personalized intelligent hotel recommendation system for online reservation. In: Proceedings of IEEE International Conference on Management and Service Science, pp. 1–5 (2010)

    Google Scholar 

  41. Xiang, Z., Schwartz, Z., Gerdes Jr., J.H., Uysal, M.: What can Big Data and Text Analytics Tell Us about Hotel Guest Experience and Satisfaction?. Int. J. Hosp. Manag. 44, 120–130 (2015)

    Article  Google Scholar 

  42. Zhou, J., Albatal, R., Gurrin, C.: Applying visual user interest profiles for recommendation and personalisation. In: Proceedings of International Conference on Multimedia Modeling, pp. 361–366 (2016)

    Chapter  Google Scholar 

  43. Zhang, K., Wang, K.Q., Wang, X.L., Jin, C.Q., Zhou, A.Y.: Hotel Recommendation based on User Preference Analysis. In: Proceedings of IEEE International Conference on Data Engineering Workshops, pp. 134–138 (2015)

    Google Scholar 

  44. Zhang, H.-L., Zhang, J., Lu, S.-J., liu, Z.-H.: Analysis of spatial autocorrelation and price determinants in hotel room prices. In: Proceedings of International Conference on Geoinformatics, pp. 1–5 (2010)

    Google Scholar 

Download references

Acknowledgments

The work was partially supported by the Ministry of Science and Technology of Taiwan under the grants MOST 103-2221-E-194-027-MY3, MOST 104-2221-E-194 -014, and MOST 105-2628-E-194-001-MY2.

Author information

Authors and Affiliations

  1. National Chung Cheng University, Chiayi, Taiwan

    Wei-Ta Chu & Wei-Han Huang

Authors
  1. Wei-Ta Chu
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Wei-Han Huang
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Wei-Ta Chu.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Chu, WT., Huang, WH. Cultural difference and visual information on hotel rating prediction. World Wide Web 20, 595–619 (2017). https://doi.org/10.1007/s11280-016-0404-2

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11280-016-0404-2

Keywords