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Towards a theory model for product search

Published: 28 March 2011 Publication History

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.

References

[1]
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.
[2]
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.
[3]
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.
[4]
Bao, S., Wu, X., Fei, B., Xue, G., Su, Z., and Yu, Y. Optimizing web search using social annotations. In WWW (2007).
[5]
Berry, S. Estimating discrete choice models of product differentiation. RAND Journal of Economics 25 (1994), 242--262.
[6]
Berry, S., Levinsohn, J., and Pakes, A. Automobile prices in market equilibrium. Econometrica 63 (1995), 841--890.
[7]
Berry, S., and Pakes, A. The pure characteristics demand model. International Economic Review 48 (2007), 1193--1225.
[8]
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.
[9]
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.
[10]
Ghose, A., and Ipeirotis, P. G. Estimating the helpfulness and economic impact of product reviews: Mining text and reviewer characteristics. IEEE TKDE (2010).
[11]
Ghose, A., Ipeirotis, P., and Sundararajan, A. Opinion mining using econometrics: A case study on reputation systems. In ACL (2007).
[12]
Hansen, L. Large sample properties of generalized method of moments estimators. Econometrica 50, 4 (1982), 1029--1054.
[13]
Heckman, J. Instrumental variables: A study of implicit behavioral assumptions used in making program evaluations. Journal of Human Resources 32, 3 (1997), 441--462.
[14]
Jin, R., Valizadegan, H., and Li, H. Ranking refinement and its application to information retrieval. In WWW (2008).
[15]
Lancaster, K. Consumer Demand: A New Approach. Columbia University Press, New York, 1971.
[16]
Li, B., Ghose, A., and Ipeirotis, P. G. Stay elsewhere? improving local search for hotels using econometric modeling and image classification. In WebDB (2008).
[17]
Marshall, A. Principles of Economics, eighth ed. Macmillan and Co., London, 1926.
[18]
McFadden, D. Conditional Logit Analysis of Qualitative Choice Behavior. Academic Press, New York, 1974.
[19]
McFadden, D., and Train, K. Mixed MNL models of discrete response. Journal of Applied Econometrics 15, 5 (2000), 447--470.
[20]
Mooney, R., and Roy, L. Content-based book recommending using learning for text categorization. In ACM SIGIR Workshop Recommender Systems: Algorithms and Evaluation (1999).
[21]
Nelder, J. A., and Mead, R. A simplex method for function minimization. The Computer Journal 7, 4 (1965).
[22]
Nie, Z., Wen, J.-R., and Ma, W.-Y. Webpage understanding: beyond page-level search. SIGMOD Record 37, 4 (2008), 48--54.
[23]
Pang, B., and Lee, L. Opinion mining and sentiment analysis. Foundations and Trends in Information Retrieval 2, 1--2 (2008).
[24]
Rosen, S. Hedonic prices and implicit markets: Product differentiation in pure competition. J. of Political Econ. 82, 1 (1974), 34--55.
[25]
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.
[26]
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.
[27]
Yee, K.-P., Swearingen, K., Li, K., and Hearst, M. Faceted metadata for image search and browsing. In CHI (2003), pp. 401--408.

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    cover image ACM Other conferences
    WWW '11: Proceedings of the 20th international conference on World wide web
    March 2011
    840 pages
    ISBN:9781450306324
    DOI:10.1145/1963405
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    Published: 28 March 2011

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    Author Tags

    1. consumer surplus
    2. economics
    3. product search
    4. ranking
    5. text mining
    6. user-generated content
    7. utility theory

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    WWW '11
    WWW '11: 20th International World Wide Web Conference
    March 28 - April 1, 2011
    Hyderabad, India

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