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

By Anindya Ghose, Panagiotis G. Ipeirotis, Beibei Li. Marketing science (Providence, R.I.), 2012

658 citations recorded by the source corpus.

Subfields: Marketing, Sociology and Political Science

View Paper: https://www.semanticscholar.org/paper/db7c8b99c7bb084996548c88b5b4b2e482fd407a

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Citations in the corpus

Estimating Demand for Mobile Applications in the New Economy (2014, background): “Similar to Ghose et al. (2012), the identification assumption is that, after controlling for app developers and consumer demographics, app store-specific valuations are independent across app stores (but are allowed to be correlated within an app store).”
Mine Your Own Business: Market-Structure Surveillance Through Text Mining (2012, methodology): “Ghose et al. (2012) used text mining together with crowdsourcing methods to estimate demand for hotels.”
A Semantic Approach for Estimating Consumer Content Preferences from Online Search Queries (2018, background): “There has been a stream of recent research in marketing that applies Natural Language Processing (NLP) to analyze online user-generated content (Archak et al., 2011; Lee and Bradlow, 2011; Netzer et al., 2012; Ghose et al., 2012).”
Large-Scale Cross-Category Analysis of Consumer Review Content on Sales Conversion Leveraging Deep Learning (2019, neutral): “Research on the relationship between product reviews and sales has been proliferate in the past decades, including Resnick and Zeckhauser (2002), Godes and Mayzlin (2004), Liu (2006), Chen and Xie (2005), Dellarocas et al.”
What makes a great MOOC? An interdisciplinary analysis of student retention in online courses (2013, background): “Finally, the hypothesis that user-generated reviews affect user choices has also received strong support in empirical studies (Dhar and Chang 2009; Ghose et al. 2012; Liu 2001; Muchnik et al. 2013).”
How is the Mobile Internet Different (2012, methodology): “Because the page number and the rank variables are highly correlated, similar to Ghose et al. (2012), we used a ‘rank-within-a-page’ variable instead of the original rank variable.”
Measuring Price Discrimination and Steering on E-commerce Web Sites (2014, background): “Several studies have looked at improving personalization on systems other than search, including targeted ads on the web [16,49], news aggregators [9,24], and even discriminatory pricing on travel search engines [15].”
The Differential Effects of the Quality and Quantity of Online Reviews on Hotel Room Sales (2014, background): “…sales performance; industry segments 366 Cornell Hospitality Quarterly 55(4) many different product types, it seems clear that customers’ expectations would also vary widely (Ghose, Ipeirotis, and Li 2012), which makes it unlikely that customer evaluations operate similarly across hotel segments.”
Cultural Differences and Geography as Determinants of Online Prosocial Lending (2014, result): “This approach addresses concerns similar to those that have been raised in other contexts, namely whether Amazon Mechanical Turkers, who are often used for large-scale data coding or survey responses, are representative of the general internet population (Behrend et al. 2011; Ghose et al. 2012).”
Advertising Content and Consumer Engagement on Social Media: Evidence from Facebook (2018, neutral): “Recent research in the social sciences has leveraged a variety of NLP methods to mine textual data and these techniques have gained traction in business research (see for e.g., Netzer et al. (2012); Archak et al. (2011); Ghose et al. (2012)).”
Evaluating Recommender Systems with User Experiments (2015, background): “[43] Users were asked which list they preferred, but also to indicate perceived differences between the lists in terms of the relative satisfaction, novelty and diversity.”
Online Consumer Review Factors Affecting Offline Hotel Popularity: Evidence from Tripadvisor (2016, background): “Ghose et al. (2012) investigate the impact of consumer reviews on a variety of products and state that less variability in the review quality could reduce risk and uncertainty associated with the hotel quality perceived by readers and thus result in higher product performance.”
Analysis of the Perceived Value of Online Tourism Reviews: Influence of Readability and Reviewer Characteristics (2016, background): “Ghose, Ipeirotis, and Li (2012) proposed a new ranking system for hotel search engines by mining consumer reviews and by considering the readability of the review as one major factor in their system.”