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Designing Ranking Systems for Hotels on Travel Search Engines by Mining User-Generated and Crowdsourced Content

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  • Anindya Ghose

    () (Stern School of Business, New York University, New York, New York 10012)

  • Panagiotis G. Ipeirotis

    () (Stern School of Business, New York University, New York, New York 10012)

  • Beibei Li

    () (Stern School of Business, New York University, New York, New York 10012)

Abstract

User-generated content on social media platforms and product search engines is changing the way consumers shop for goods online. However, current product search engines fail to effectively leverage information created across diverse social media platforms. Moreover, current ranking algorithms in these product search engines tend to induce consumers to focus on one single product characteristic dimension (e.g., price, star rating). This approach largely ignores consumers' multidimensional preferences for products. In this paper, we propose to generate a ranking system that recommends products that provide, on average, the best value for the consumer's money. The key idea is that products that provide a higher surplus should be ranked higher on the screen in response to consumer queries. We use a unique data set of U.S. hotel reservations made over a three-month period through Travelocity, which we supplement with data from various social media sources using techniques from text mining, image classification, social geotagging, human annotations, and geomapping. We propose a random coefficient hybrid structural model, taking into consideration the two sources of consumer heterogeneity the different travel occasions and different hotel characteristics introduce. Based on the estimates from the model, we infer the economic impact of various location and service characteristics of hotels. We then propose a new hotel ranking system based on the average utility gain a consumer receives from staying in a particular hotel. By doing so, we can provide customers with the "best-value" hotels early on. Our user studies, using ranking comparisons from several thousand users, validate the superiority of our ranking system relative to existing systems on several travel search engines. On a broader note, this paper illustrates how social media can be mined and incorporated into a demand estimation model in order to generate a new ranking system in product search engines. We thus highlight the tight linkages between user behavior on social media and search engines. Our interdisciplinary approach provides several insights for using machine learning techniques in economics and marketing research.

Suggested Citation

  • Anindya Ghose & Panagiotis G. Ipeirotis & Beibei Li, 2012. "Designing Ranking Systems for Hotels on Travel Search Engines by Mining User-Generated and Crowdsourced Content," Marketing Science, INFORMS, vol. 31(3), pages 493-520, May.
  • Handle: RePEc:inm:ormksc:v:31:y:2012:i:3:p:493-520
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    File URL: http://dx.doi.org/10.1287/mksc.1110.0700
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Rahat Ullah & Wonjoon Kim & Naveen C. Amblee & Hyunjong Lee & Alice Oh, 2014. "Do Emotions Matter? Exploring The Distribution Of Emotions In Online Product Reviews," Working papers 156, Indian Institute of Management Kozhikode.
    2. O. Loginova & A. Mantovani, 2015. "Information and Online Reviews," Working Papers wp996, Dipartimento Scienze Economiche, Universita' di Bologna.
    3. repec:eee:touman:v:59:y:2017:i:c:p:57-66 is not listed on IDEAS
    4. Oksana Loginova & Andrea Mantovani, 2015. "Price Competition in the Presence of a Web Aggregator," Working Papers 1616, Department of Economics, University of Missouri, revised 17 Aug 2016.
    5. Anuj Kapoor & Catherine Tucker, 2017. "How do Platform Participants respond to an Unfair Rating? An Analysis of a Ride-Sharing Platform Using a Quasi-Experiment," Working Papers 17-19, NET Institute.
    6. repec:eee:ijrema:v:34:y:2017:i:1:p:265-285 is not listed on IDEAS
    7. repec:eee:touman:v:56:y:2016:i:c:p:40-51 is not listed on IDEAS
    8. repec:eee:touman:v:52:y:2016:i:c:p:498-506 is not listed on IDEAS
    9. Ben Shiller, 2016. "Personalized Price Discrimination Using Big Data," Working Papers 108, Brandeis University, Department of Economics and International Businesss School.
    10. repec:eee:jbvent:v:33:y:2018:i:3:p:371-393 is not listed on IDEAS
    11. repec:eee:proeco:v:191:y:2017:i:c:p:97-112 is not listed on IDEAS
    12. Oded Netzer & Ronen Feldman & Jacob Goldenberg & Moshe Fresko, 2012. "Mine Your Own Business: Market-Structure Surveillance Through Text Mining," Marketing Science, INFORMS, vol. 31(3), pages 521-543, May.
    13. repec:eee:jbrese:v:84:y:2018:i:c:p:24-33 is not listed on IDEAS
    14. Michael Luca & Timothy Wu & Sebastian Couvidat & Daniel Frank & William Seltzer, 2015. "Does Google Content Degrade Google Search? Experimental Evidence," Harvard Business School Working Papers 16-035, Harvard Business School, revised Aug 2016.
    15. repec:eee:joinma:v:37:y:2017:i:c:p:16-31 is not listed on IDEAS
    16. Michael Luca & Georgios Zervas, 2013. "Fake It Till You Make It: Reputation, Competition, and Yelp Review Fraud," Harvard Business School Working Papers 14-006, Harvard Business School, revised May 2015.
    17. Anja Lambrecht & Avi Goldfarb & Alessandro Bonatti & Anindya Ghose & Daniel Goldstein & Randall Lewis & Anita Rao & Navdeep Sahni & Song Yao, 2014. "How do firms make money selling digital goods online?," Marketing Letters, Springer, vol. 25(3), pages 331-341, September.
    18. Scholz, Michael & Pfeiffer, Jella & Rothlauf, Franz, 2017. "Using PageRank for non-personalized default rankings in dynamic markets," European Journal of Operational Research, Elsevier, vol. 260(1), pages 388-401.
    19. Amedeo Piolatto, 2015. "Online booking and information: competition and welfare consequences of review aggregators," Working Papers 2015/11, Institut d'Economia de Barcelona (IEB).

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