Designing Ranking Systems for Hotels on Travel Search Engines by Mining User-Generated and Crowdsourced Content
AbstractUser-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.
Download InfoIf you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
Bibliographic InfoArticle provided by INFORMS in its journal Marketing Science.
Volume (Year): 31 (2012)
Issue (Month): 3 (May)
user-generated content; social media; search engines; hotels; ranking system; structural models; text mining; crowdsourcing;
You can help add them by filling out this form.
CitEc Project, subscribe to its RSS feed for this item.
- 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.
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.