ABSTRACT
With the growing pervasiveness of the Internet, online search for products and services is constantly increasing. Most product search engines are based on adaptations of theoretical models devised for information retrieval. However, the decision mechanism that underlies the process of buying a product is different than the process of locating relevant documents or objects.
We propose a theory model for product search based on expected utility theory from economics. Specifically, we propose a ranking technique in which we rank highest the products that generate the highest surplus, after the purchase. In a sense, the top ranked products are the "best value for money" for a specific user. Our approach builds on research on "demand estimation" from economics and presents a solid theoretical foundation on which further research can build on. We build algorithms that take into account consumer demographics, heterogeneity of consumer preferences, and also account for the varying price of the products. We show how to achieve this without knowing the demographics or purchasing histories of individual consumers but by using aggregate demand data. We evaluate our work, by applying the techniques on hotel search. Our extensive user studies, using more than 15,000 user-provided ranking comparisons, demonstrate an overwhelming preference for the rankings generated by our techniques, compared to a large number of existing strong state-of-the-art baselines.
- Adomavicius, G., and Tuzhilin, A. Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions. IEEE Transactions on Knowledge and Data Engineering 17 (2005), 734--749. Google ScholarDigital Library
- Archak, N., Ghose, A., and Ipeirotis, P. G. Show me the money!: deriving the pricing power of product features by mining consumer reviews. In KDD (2007), pp. 56--65. Google ScholarDigital Library
- Balke, W.-T., and Güntzer, U. Multi-objective query processing for database systems. In Proceedings of 28th International Conference on Very Large Data Bases (VLDB) (2004), pp. 936--947. Google ScholarDigital Library
- Bao, S., Wu, X., Fei, B., Xue, G., Su, Z., and Yu, Y. Optimizing web search using social annotations. In WWW (2007). Google ScholarDigital Library
- Berry, S. Estimating discrete choice models of product differentiation. RAND Journal of Economics 25 (1994), 242--262.Google ScholarCross Ref
- Berry, S., Levinsohn, J., and Pakes, A. Automobile prices in market equilibrium. Econometrica 63 (1995), 841--890.Google ScholarCross Ref
- Berry, S., and Pakes, A. The pure characteristics demand model. International Economic Review 48 (2007), 1193--1225.Google ScholarCross Ref
- Chevalier, J. A., and Goolsbee, A. Measuring prices and price competition online: Amazon.com and BarnesandNoble.com. Quantitative Marketing and Economics 1, 2 (2003), 203--222.Google ScholarCross Ref
- Forman, C., Ghose, A., and Wiesenfeld, B. Examining the relationship between reviews and sales: the role of reviewer identity disclosure in electronic markets. ISR 19, 3 (2008), 291--313.Google ScholarCross Ref
- Ghose, A., and Ipeirotis, P. G. Estimating the helpfulness and economic impact of product reviews: Mining text and reviewer characteristics. IEEE TKDE (2010). Google ScholarDigital Library
- Ghose, A., Ipeirotis, P., and Sundararajan, A. Opinion mining using econometrics: A case study on reputation systems. In ACL (2007).Google Scholar
- Hansen, L. Large sample properties of generalized method of moments estimators. Econometrica 50, 4 (1982), 1029--1054.Google ScholarCross Ref
- Heckman, J. Instrumental variables: A study of implicit behavioral assumptions used in making program evaluations. Journal of Human Resources 32, 3 (1997), 441--462.Google Scholar
- Jin, R., Valizadegan, H., and Li, H. Ranking refinement and its application to information retrieval. In WWW (2008). Google ScholarDigital Library
- Lancaster, K. Consumer Demand: A New Approach. Columbia University Press, New York, 1971.Google Scholar
- Li, B., Ghose, A., and Ipeirotis, P. G. Stay elsewhere? improving local search for hotels using econometric modeling and image classification. In WebDB (2008).Google Scholar
- Marshall, A. Principles of Economics, eighth ed. Macmillan and Co., London, 1926.Google Scholar
- McFadden, D. Conditional Logit Analysis of Qualitative Choice Behavior. Academic Press, New York, 1974.Google Scholar
- McFadden, D., and Train, K. Mixed MNL models of discrete response. Journal of Applied Econometrics 15, 5 (2000), 447--470.Google ScholarCross Ref
- Mooney, R., and Roy, L. Content-based book recommending using learning for text categorization. In ACM SIGIR Workshop Recommender Systems: Algorithms and Evaluation (1999).Google Scholar
- Nelder, J. A., and Mead, R. A simplex method for function minimization. The Computer Journal 7, 4 (1965).Google ScholarCross Ref
- Nie, Z., Wen, J.-R., and Ma, W.-Y. Webpage understanding: beyond page-level search. SIGMOD Record 37, 4 (2008), 48--54. Google ScholarDigital Library
- Pang, B., and Lee, L. Opinion mining and sentiment analysis. Foundations and Trends in Information Retrieval 2, 1--2 (2008). Google ScholarDigital Library
- Rosen, S. Hedonic prices and implicit markets: Product differentiation in pure competition. J. of Political Econ. 82, 1 (1974), 34--55.Google ScholarCross Ref
- Song, M. A hybrid discrete choice model of differentiated product demand with an application to personal computers. Simon School Working Paper No. FR 08-09, 2008.Google Scholar
- Ye, Q., Law, R., and Gu, B. The impact of online user reviews on hotel room sales. Int. J. of Hosp. Mgmnt. 28, 1 (2009), 180--182.Google ScholarCross Ref
- Yee, K.-P., Swearingen, K., Li, K., and Hearst, M. Faceted metadata for image search and browsing. In CHI (2003), pp. 401--408. Google ScholarDigital Library
Index Terms
- Towards a theory model for product search
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