ABSTRACT
Today, many e-commerce websites personalize their content, including Netflix (movie recommendations), Amazon (product suggestions), and Yelp (business reviews). In many cases, personalization provides advantages for users: for example, when a user searches for an ambiguous query such as ``router,'' Amazon may be able to suggest the woodworking tool instead of the networking device. However, personalization on e-commerce sites may also be used to the user's disadvantage by manipulating the products shown (price steering) or by customizing the prices of products (price discrimination). Unfortunately, today, we lack the tools and techniques necessary to be able to detect such behavior.
In this paper, we make three contributions towards addressing this problem. First, we develop a methodology for accurately measuring when price steering and discrimination occur and implement it for a variety of e-commerce web sites. While it may seem conceptually simple to detect differences between users' results, accurately attributing these differences to price discrimination and steering requires correctly addressing a number of sources of noise. Second, we use the accounts and cookies of over 300 real-world users to detect price steering and discrimination on 16 popular e-commerce sites. We find evidence for some form of personalization on nine of these e-commerce sites. Third, we investigate the effect of user behaviors on personalization. We create fake accounts to simulate different user features including web browser/OS choice, owning an account, and history of purchased or viewed products. Overall, we find numerous instances of price steering and discrimination on a variety of top e-commerce sites.
- L. Andrade and M. J. Silva. Relevance Ranking for Geographic IR. GIR, 2006.Google Scholar
- Amazon mechanical turk. http://mturk.com/.Google Scholar
- F. Bakalov, M.-J. Meurs, B. König-Ries, B. Satel, R. G. Butler, and A. Tsang. An Approach to Controlling User Models and Personalization Effects in Recommender Systems. IUI, 2013. Google ScholarDigital Library
- K. Bhasin. JCPenney Execs Admit They Didn't Realize How Much Customers Were Into Coupons. Business Insider, 2012. http://www.businessinsider.com/jcpenney-didnt-realize-how-much-customers-were-into-coupons-2012--5.Google Scholar
- P. Belobaba, A. Odoni, and C. Barnhart. The Global Airline Industry. Wiley, 2009.Google ScholarCross Ref
- C. Chiames. Correspondence with the authors, in reference to a pre-publication version of this manuscript, 2014. http://personalization.ccs.neu.edu/orbitz_letter.pdf.Google Scholar
- R. Calo. Digital Market Manipulation. The George Washington Law Review, 82, 2014.Google Scholar
- A. Das, M. Datar, A. Garg, and S. Rajaram. Google News Personalization: Scalable Online Collaborative Filtering. WWW, 2007. Google ScholarDigital Library
- C. Duhigg. How Companies Learn Your Secrets. The New York Times, 2012. http://www.nytimes.com/2012/02/19/magazine/shopping-habits.html.Google Scholar
- P. Diaconis and R. L. Graham. Spearman's Footrule as a Measure of Disarray. J. Roy. Stat. B, 39(2), 1977.Google Scholar
- Z. Dou, R. Song, and J.-R. Wen. A Large-scale Evaluation and Analysis of Personalized Search Strategies. WWW, 2007. Google ScholarDigital Library
- J. H. Dorfman. Economics and Management of the Food Industry. Routledge, 2013.Google Scholar
- R. Fagin, R. Kumar, and D. Sivakumar. Comparing top k lists. SODA, 2003. Google ScholarDigital Library
- A. Ghose, P. G. Ipeirotis, and B. Li. Designing Ranking Systems for Hotels on Travel Search Engines by Mining User-Generated and Crowdsourced Content. Marketing Science, 31(3), 2012. Google ScholarDigital Library
- S. Guha, B. Cheng, and P. Francis. Challenges in Measuring Online Advertising Systems. IMC, 2010. Google ScholarDigital Library
- A. Hannak, P. Sapiezy\'nski, A. M. Kakhki, B. Krishnamurthy, D. Lazer, A. Mislove, and C. Wilson. Measuring Personalization of Web Search. WWW, 2013. Google ScholarDigital Library
- J. Hu, H.-J. Zeng, H. Li, C. Niu, and Z. Chen. Demographic Prediction Based on User's Browsing Behavior. WWW, 2007. Google ScholarDigital Library
- K. J\"arvelin and J. Kek\"al\"ainen. IR evaluation methods for retrieving highly relevant documents. SIGIR, 2000. Google ScholarDigital Library
- K. J\"arvelin and J. Kek\"al\"ainen. Cumulated Gain-based Evaluation of IR Techniques. ACM TOIS, 20(4), 2002. Google ScholarDigital Library
- R. Kumar and S. Vassilvitskii. Generalized Distances Between Rankings. WWW, 2010. Google ScholarDigital Library
- M. G. Kendall. A New Measure of Rank Correlation. Biometrika, 30(1/2), 1938.Google Scholar
- W. H. Kruskal. Ordinal Measures of Association. Journal of the American Statistical Association, 53(284), 1958.Google Scholar
- L. Li, W. Chu, J. Langford, and R. E. Schapire. A Contextual-Bandit Approach to Personalized News Article Recommendation. WWW, 2010. Google ScholarDigital Library
- B. D. Lollis. Orbitz: Mac users book fancier hotels than PC users. USA Today Travel Blog, 2012. http://travel.usatoday.com/hotels/post/2012/05/orbitz-hotel-booking-mac-pc-/690633/1.Google Scholar
- B. D. Lollis. Orbitz: Mobile searches may yield better hotel deals. USA Today Travel Blog, 2012. http://travel.usatoday.com/hotels/post/2012/05/orbitz-mobile-hotel-deals/691470/1.Google Scholar
- A. Majumder and N. Shrivastava. Know your personalization: learning topic level personalization in online services. WWW, 2013. Google ScholarDigital Library
- D. Mattioli. On Orbitz, Mac Users Steered to Pricier Hotels. The Wall Street Journal, 2012. http://on.wsj.com/LwTnPH.Google Scholar
- J. Mikians, L. Gyarmati, V. Erramilli, and N. Laoutaris. Detecting Price and Search Discrimination on the Internet. HotNets, 2012. Google ScholarDigital Library
- J. Mikians, L. Gyarmati, V. Erramilli, and N. Laoutaris. Crowd-assisted Search for Price Discrimination in E-Commerce: First results. CoNEXT, 2013. Google ScholarDigital Library
- E. Pariser. The Filter Bubble: What the Internet is Hiding from You. Penguin Press, 2011. Google ScholarDigital Library
- I. Png. Managerial Economics. Routledge, 2012.Google Scholar
- J. Pitkow, H. Schütze, T. Cass, R. Cooley, D. Turnbull, A. Edmonds, E. Adar, and T. Breuel. Personalized search. CACM, 45(9), 2002. Google ScholarDigital Library
- Panopticlick. https://panopticlick.eff.org.Google Scholar
- PhantomJS. 2013. http://phantomjs.org.Google Scholar
- A. Ramasastry. Web sites change prices based on customers' habits. CNN, 2005. http://edition.cnn.com/2005/LAW/06/24/ramasastry.website.prices/.Google Scholar
- C. Shapiro and H. R. Varian. Information Rules: A Strategic Guide to the Network Economy. Harvard Business School Press, 1999. Google ScholarDigital Library
- C. Spearman. The Proof and Measurement of Association between Two Things. Am J Psychol, 15, 1904.Google Scholar
- D. Sculley. Rank Aggregation for Similar Items. SDM, 2007.Google ScholarCross Ref
- G. S. Shieh, Z. Bai, and W.-Y. Tsai. Rank Tests for Independence--With a Weighted Contamination Alternative. Stat. Sinica, 10, 2000.Google Scholar
- Selenium. 2013. http://selenium.org.Google Scholar
- B. Tan, X. Shen, and C. Zhai. Mining long-term search history to improve search accuracy. KDD, 2006. Google ScholarDigital Library
- Top 500 e-retailers. http://www.top500guide.com/top-500/.Google Scholar
- Top booking sites. http://skift.com/2013/11/11/top-25-online-booking-sites-in-travel/.Google Scholar
- T. Vissers, N. Nikiforakis, N. Bielova, and W. Joosen. Crying Wolf? On the Price Discrimination of Online Airline Tickets. HotPETs, 2014.Google Scholar
- J. Valentino-Devries, J. Singer-Vine, and A. Soltani. Websites Vary Prices, Deals Based on Users' Information. Wall Street Journal, 2012. http://online.wsj.com/news/articles/SB10001424127887323777204578189391813881534.Google Scholar
- I. Weber and A. Jaimes. Who Uses Web Search for What? And How? WSDM, 2011. Google ScholarDigital Library
- T. Wadhwa. How Advertisers Can Use Your Personal Information to Make You Pay Higher Prices. Huffington Post, 2014. http://www.huffingtonpost.com/tarun-wadhwa/how-advertisers-can-use-y_b_4703013.html.Google Scholar
- C. E. Wills and C. Tatar. Understanding What They Do with What They Know. WPES, 2012. Google ScholarDigital Library
- X. Xing, W. Meng, D. Doozan, N. Feamster, W. Lee, and A. C. Snoeren. Exposing Inconsistent Web Search Results with Bobble. PAM, 2014.Google ScholarDigital Library
- X. Xing, W. Meng, D. Doozan, A. C. Snoeren, N. Feamster, and W. Lee. Take This Personally: Pollution Attacks on Personalized Services. USENIX Security, 2013. Google ScholarDigital Library
- Y. Xu, B. Zhang, Z. Chen, and K. Wang. Privacy-Enhancing Personalized Web Search. WWW, 2007. Google ScholarDigital Library
- B. Yu and G. Cai. A query-aware document ranking method for geographic information retrieval. GIR, 2007. Google ScholarDigital Library
- X. Yi, H. Raghavan, and C. Leggetter. Discovering Users' Specific Geo Intention in Web Search. WWW, 2009. Google ScholarDigital Library
Index Terms
- Measuring Price Discrimination and Steering on E-commerce Web Sites
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