Skip to main content
Log in

Ranking hotels through multi-dimensional hotel information: a method considering travelers’ preferences and expectations

  • Original Research
  • Published:
Information Technology & Tourism Aims and scope Submit manuscript

Abstract

Although travelers tend to consider multi-dimensional hotel information when choosing their accommodation, few online travel agency (OTA) websites allow them to express their preferences and expectations for the selection criteria to obtain customized hotel ranking results. The lack of this function makes travelers have to spend extra time and effort in comparing different hotels to make the final decision. To solve this problem, a hotel ranking method considering travelers’ preferences and expectations is proposed based on multi-dimensional hotel information. In the method, considering the travelers’ actual process of hotel reservation through the OTA website, four types of hotel information (i.e., price, rating, location and text comment) are used. To make full use of these information, text mining, prospect theory and multi-attribute decision-making method are integrated into the proposed method. A case study is given to verify the reliability of the proposed method. The proposed method can be embedded into OTA websites to provide decision support for travelers’ hotel reservation, which will reduce the time spent by travelers in hotel search and comparison, thus effectively promote hotel reservation and improve traveler satisfaction.

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

Access this article

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

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

References

  • Beldona S, Morrison AM, O’Leary J (2005) Online shopping motivations and pleasure travel products: a correspondence analysis. Tour Manag 26(4):561–570

    Article  Google Scholar 

  • Bi JW, Liu Y, Fan ZP (2019a) Representing sentiment analysis results of online reviews using interval type-2 fuzzy numbers and its application to product ranking. Inf Sci 504:293–307

    Article  Google Scholar 

  • Bi JW, Liu Y, Fan ZP, Zhang J (2019b) Wisdom of crowds: conducting importance-performance analysis (IPA) through online reviews. Tour Manag 70:460–478

    Article  Google Scholar 

  • Bi JW, Liu Y, Li H (2020) Daily tourism volume forecasting for tourist attractions. Ann Tourism Res 83:102923

    Article  Google Scholar 

  • Bi JW, Li C, Xu H, Li H (2021) Forecasting daily tourism demand for tourist attractions with big data: an ensemble deep learning method. J Travel Res 00472875211040569

  • Cambria E, Poria S, Hazarika D, Kwok K (2018) SenticNet 5: discovering conceptual primitives for sentiment analysis by means of context embeddings. In: Thirty-second AAAI conference on artificial intelligence

  • Chang YW, Hsu PY, Lan YC (2019) Cooperation and competition between online travel agencies and hotels. Tour Manag 71:187–196

    Article  Google Scholar 

  • Childers TL, Carr CL, Peck J, Carson S (2001) Hedonic and utilitarian motivations for online retail shopping behavior. J Retail 77(4):511–535

    Article  Google Scholar 

  • Fan ZP, Zhang X, Chen FD, Liu Y (2013) Multiple attribute decision making considering aspiration-levels: a method based on prospect theory. Comput Ind Eng 65(2):341–350

    Article  Google Scholar 

  • Fazzolari M, Petrocchi M (2018) A study on online travel reviews through intelligent data analysis. Inf Technol Tour 20(1):37–58

    Article  Google Scholar 

  • Ghose A, Ipeirotis PG, Li B (2012) Designing ranking systems for hotels on travel search engines by mining user-generated and crowdsourced content. Mark Sci 31(3):493–520

    Article  Google Scholar 

  • Guillet BD, Mattila A, Gao L (2019) The effects of choice set size and information filtering mechanisms on online hotel booking. Int J Hosp Manag 87:102379

    Article  Google Scholar 

  • Hou Z, Cui F, Meng Y, Lian T, Yu C (2019) Opinion mining from online travel reviews: a comparative analysis of Chinese major OTAs using semantic association analysis. Tour Manag 74:276–289

    Article  Google Scholar 

  • Hu XS, Yang Y (2019) Determinants of consumers’ choices in hotel online searches: a comparison of consideration and booking stages. Int J Hosp Manag 86:102370

    Article  Google Scholar 

  • Kahneman D, Tversky A (1979) Prospect theory: an analysis of decision under risk. Econometrica 47(2):263–292

    Article  Google Scholar 

  • Kim J, Franklin D, Phillips M, Hwang E (2020) Online travel agency price presentation: examining the influence of price dispersion on travelers’ hotel preference. J Travel Res 59(4):704–721

    Article  Google Scholar 

  • Kwok PK, Lau HY (2019) Hotel selection using a modified TOPSIS-based decision support algorithm. Decis Support Syst 120:95–105

    Article  Google Scholar 

  • Li MY, Cao PP (2019) Extended TODIM method for multi-attribute risk decision making problems in emergency response. Comput Ind Eng 135:1286–1293

    Article  Google Scholar 

  • Liang X, Liu P, Wang Z (2019) Hotel selection utilizing online reviews: a novel decision support model based on sentiment analysis and DL-VIKOR method. Technol Econ Dev Econ 25(6):1139–1161

    Article  Google Scholar 

  • Liu P, Teng F (2019) Probabilistic linguistic TODIM method for selecting products through online product reviews. Inf Sci 485:441–455

    Article  Google Scholar 

  • Liu JN, Zhang EY (2014) An investigation of factors affecting customer selection of online hotel booking channels. Int J Hosp Manag 39:71–83

    Article  Google Scholar 

  • Liu Y, Bi JW, Fan ZP (2017) Ranking products through online reviews: a method based on sentiment analysis technique and intuitionistic fuzzy set theory. Inf Fusion 36:149–161

    Article  Google Scholar 

  • Mellinas JP, Nicolau JL, Park S (2019) Inconsistent behavior in online consumer reviews: the effects of hotel attribute ratings on location. Tour Manag 71:421–427

    Article  Google Scholar 

  • Nguyen Q (2016) Linking loss aversion and present bias with overspending behavior of tourists: insights from a lab-in-the-field experiment. Tour Manag 54:152–159

    Article  Google Scholar 

  • Poyry, E., Parvinen, P., Salo, J., & Blakaj, H. (2012). Hedonic and utilitarian search for electronic word-of-mouth. In: 2012 45th Hawaii international conference on system sciences, pp 1797–1806

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

    Article  Google Scholar 

  • Sharma A, Nicolau JL (2019) Hotels to OTAs: “Hands off my rates!” The economic consequences of the rate parity legislative actions in Europe and the US. Tour Manag 75:427–434

    Article  Google Scholar 

  • Thelwall M, Buckley K, Paltoglou G, Cai D, Kappas A (2010) Sentiment strength detection in short informal text. J Am Soc Inform Sci Technol 61(12):2544–2558

    Article  Google Scholar 

  • Tversky A, Kahneman D (1991) Loss aversion in riskless choice: a reference dependent model. Q J Econ 106(4):1039–1061

    Article  Google Scholar 

  • Tversky A, Kahneman D (1992) Advances in prospect theory: cumulative representation of uncertainty. J Risk Uncertain 5(4):297–323

    Article  Google Scholar 

  • Wang R (2018) When prospect theory meets consumer choice models: assortment and pricing management with reference prices. Manuf Serv Oper Manag 20(3):583–600

    Article  Google Scholar 

  • Wang L, Wang XK, Peng JJ, Wang JQ (2020) The differences in hotel selection among various types of travellers: a comparative analysis with a useful bounded rationality behavioural decision support model. Tour Manag 76:103961

    Article  Google Scholar 

  • Xiang Z, Magnini VP, Fesenmaier DR (2015) Information technology and consumer behavior in travel and tourism: insights from travel planning using the internet. J Retail Consum Serv 22:244–249

    Article  Google Scholar 

  • Zheng W, Ji H, Lin C, Wang W, Yu B (2020) Using a heuristic approach to design personalized urban tourism itineraries with hotel selection. Tour Manag 76:103956

    Article  Google Scholar 

Download references

Acknowledgements

This work was partly supported by the National Natural Science Foundation of China (project no. 72101124), Humanities and Social Science Fund of Ministry of Education of China (project no. 20YJC630002), the China Postdoctoral Science Foundation (project nos. 2020T130318 and 2019M661000), the Liberal Arts Development Fund of Nankai University (project no. ZX20210067).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Tian-Yu Han.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Bi, JW., Han, TY., Yao, Y. et al. Ranking hotels through multi-dimensional hotel information: a method considering travelers’ preferences and expectations. Inf Technol Tourism 24, 127–155 (2022). https://doi.org/10.1007/s40558-022-00223-y

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s40558-022-00223-y

Keywords

Navigation