Designing Ranking Systems for Hotels on Travel Search Engines by Mining User-Generated and Crowdsourced Content
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.
Volume (Year): 31 (2012)
Issue (Month): 3 (May)
|Contact details of provider:|| Postal: 7240 Parkway Drive, Suite 300, Hanover, MD 21076 USA|
Web page: http://www.informs.org/
More information through EDIRC
References listed on IDEAS
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Steve Berry & Oliver Linton & Ariel Pakes, 2000.
"Limit theorems for estimating the parameters of differentiated product demand systems,"
LSE Research Online Documents on Economics
2032, London School of Economics and Political Science, LSE Library.
- Steve Berry & Oliver B. Linton & Ariel Pakes, 2004. "Limit Theorems for Estimating the Parameters of Differentiated Product Demand Systems," Review of Economic Studies, Oxford University Press, vol. 71(3), pages 613-654.
- Steven Berry & Oliver Linton & Ariel Pakes, 2002. "Limit Theorems for Estimating the Parameters of Differentiated Product Demand Systems," Cowles Foundation Discussion Papers 1372, Cowles Foundation for Research in Economics, Yale University.
- Steve Berry & Oliver Linton & Ariel Pakes, 2000. "Limit Theorems for Estimating the Parameters of Differentiated Product Demand Systems," STICERD - Econometrics Paper Series 400, Suntory and Toyota International Centres for Economics and Related Disciplines, LSE.
- Steve Berry & Oliver B. Linton & Ariel Pakes, 2002. "Limit Theorems for Estimating the Parameters of Differentiated Product Demand Systems," Harvard Institute of Economic Research Working Papers 1955, Harvard - Institute of Economic Research.
- Nevo, Aviv, 1998.
"Measuring Market Power in the Ready-To-Eat Cereal Industry,"
25164, University of Connecticut, Food Marketing Policy Center.
- Nevo, Aviv, 2001. "Measuring Market Power in the Ready-to-Eat Cereal Industry," Econometrica, Econometric Society, vol. 69(2), pages 307-342, March.
- Aviv Nevo, 1998. "Measuring Market Power in the Ready-to-Eat Cereal Industry," NBER Working Papers 6387, National Bureau of Economic Research, Inc.
- Aviv Nevo, 2003. "Measuring Market Power in the Ready-to-Eat Cereal Industry," Microeconomics 0303006, EconWPA.
- Nevo, Aviv, 1998. "Measuring Market Power in the Ready-To-Eat Cereal Industry," Food Marketing Policy Center Research Reports 037, University of Connecticut, Department of Agricultural and Resource Economics, Charles J. Zwick Center for Food and Resource Policy.
- Nevo, Aviv, 1999. "Measuring Market Power in the Ready-to-Eat Cereal Industry," Competition Policy Center, Working Paper Series qt7cm5p858, Competition Policy Center, Institute for Business and Economic Research, UC Berkeley.
- Tuck Siong Chung & Roland T. Rust & Michel Wedel, 2009. "My Mobile Music: An Adaptive Personalization System for Digital Audio Players," Marketing Science, INFORMS, vol. 28(1), pages 52-68, 01-02.
- HernÃ¡n A. Bruno & Naufel J. Vilcassim, 2008. "—Structural Demand Estimation with Varying Product Availability," Marketing Science, INFORMS, vol. 27(6), pages 1126-1131, 11-12.
- Michael Luca, 2011. "Reviews, Reputation, and Revenue: The Case of Yelp.com," Harvard Business School Working Papers 12-016, Harvard Business School, revised Mar 2016.
- Roland T. Rust & Tuck Siong Chung, 2006. "Marketing Models of Service and Relationships," Marketing Science, INFORMS, vol. 25(6), pages 560-580, 11-12.
- Wesley Hartmann & Harikesh S. Nair & Sridhar Narayanan, 2011. "Identifying Causal Marketing Mix Effects Using a Regression Discontinuity Design," Marketing Science, INFORMS, vol. 30(6), pages 1079-1097, November.
- Nikolay Archak & Anindya Ghose & Panagiotis G. Ipeirotis, 2011. "Deriving the Pricing Power of Product Features by Mining Consumer Reviews," Management Science, INFORMS, vol. 57(8), pages 1485-1509, August.
- McCrary, Justin, 2008. "Manipulation of the running variable in the regression discontinuity design: A density test," Journal of Econometrics, Elsevier, vol. 142(2), pages 698-714, February.
- Jehoshua Eliashberg & Sam K. Hui & Z. John Zhang, 2007. "From Story Line to Box Office: A New Approach for Green-Lighting Movie Scripts," Management Science, INFORMS, vol. 53(6), pages 881-893, June.
- Harikesh Nair & Jean-Pierre Dubé & Pradeep Chintagunta, 2005.
"Accounting for Primary and Secondary Demand Effects with Aggregate Data,"
INFORMS, vol. 24(3), pages 444-460, November.
- Nair, Harikesh S. & Dube, Jean-Pierre & Chintagunta, Pradeep, 2004. "Accounting for Primary and Secondary Demand Effects with Aggregate Data," Research Papers 1949, Stanford University, Graduate School of Business.
- 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.
- Susan Athey & Guido W. Imbens, 2007.
"Discrete Choice Models With Multiple Unobserved Choice Characteristics,"
International Economic Review,
Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 48(4), pages 1159-1192, November.
- Susan Athey & Guido Imbens, 2006. "Discrete Choice Models with Multiple Unobserved Choice Characteristics," Levine's Bibliography 122247000000001040, UCLA Department of Economics.
- Berry, Steven & Levinsohn, James & Pakes, Ariel, 1995. "Automobile Prices in Market Equilibrium," Econometrica, Econometric Society, vol. 63(4), pages 841-890, July.
- Joel H. Steckel & Wilfried R. Vanhonacker, 1993. "Cross-Validating Regression Models in Marketing Research," Marketing Science, INFORMS, vol. 12(4), pages 415-427.
- David Godes & Dina Mayzlin, 2004. "Using Online Conversations to Study Word-of-Mouth Communication," Marketing Science, INFORMS, vol. 23(4), pages 545-560, June.
- Steven Berry & Ariel Pakes, 2007. "The Pure Characteristics Demand Model," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 48(4), pages 1193-1225, November.
- J. Miguel Villas-Boas & Russell S. Winer, 1999. "Endogeneity in Brand Choice Models," Management Science, INFORMS, vol. 45(10), pages 1324-1338, October.
- Nikolay Archak & Anindya Ghose & Panagiotis G. Ipeirotis, 2007. "Deriving the Pricing Power of Product Features by Mining Consumer Reviews," Working Papers 07-36, NET Institute.
- Pradeep Chintagunta & Jean-Pierre Dubé & Khim Yong Goh, 2005. "Beyond the Endogeneity Bias: The Effect of Unmeasured Brand Characteristics on Household-Level Brand Choice Models," Management Science, INFORMS, vol. 51(5), pages 832-849, May.
- Sanjiv R. Das & Mike Y. Chen, 2007. "Yahoo! for Amazon: Sentiment Extraction from Small Talk on the Web," Management Science, INFORMS, vol. 53(9), pages 1375-1388, September.
When requesting a correction, please mention this item's handle: RePEc:inm:ormksc:v:31:y:2012:i:3:p:493-520. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Mirko Janc)
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If references are entirely missing, you can add them using this form.
If the full references list an item that is present in RePEc, but the system did not link to it, you can help with this form.
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your profile, as there may be some citations waiting for confirmation.
Please note that corrections may take a couple of weeks to filter through the various RePEc services.