Selling vs. Profiling: Optimizing the Offer Set in Web-Based Personalization

References

• Abernathy J, Evgeniou T, Toubia O, Vert J-P (2008) Eliciting consumer preferences using robust adaptive choice questionnaires. IEEE Trans. Knowledge Data Engrg. 20(2):145–155.Crossref, Google Scholar
• Adomavicius G, Tuzhilin A (2005) Personalization technologies: A process-oriented perspective. Comm. ACM 48(10):83–90.Crossref, Google Scholar
• Ansari A, Mela CF (2003) E-customization. J. Marketing Res. 40(2):131–145.Crossref, Google Scholar
• Atahan P, Sarkar S (2011) Accelerated learning of user profiles. Management Sci. 57(2):215–239.Link, Google Scholar
• Balabanovic M (1998) Exploring versus exploiting when learning user models for text recommendation. User Modeling User-Adapted Interaction 8:71–102.Crossref, Google Scholar
• Blattberg RC, Deighton J (1996) Manage marketing by the customer equity test. Harvard Bus. Rev. 74(4):136–144.Google Scholar
• Bodapati A (2008) Recommendation systems with purchase data. J. Marketing Res. XLV:77–93.Crossref, Google Scholar
• Breese JS, Heckerman D, Kadie C (1998) Empirical analysis of predictive algorithms for collaborative filtering. Proc. 14th Conf. Uncertainty in Artificial Intelligence (Morgan Kaufmann, San Francisco), 43–52.Google Scholar
• Caro F, Gallien J (2007) Dynamic assortment with demand learning for seasonal consumer goods. Management Sci. 53(2):276–292.Link, Google Scholar
• Case JH (1979) Economics and the Competitive Process (New York University Press, New York).Google Scholar
• Das A, Mathieu C, Ricketts D (2010) Maximizing profit using recommender systems. WWW2010.Google Scholar
• Fader PS, Hardie BGS, Lee KL (2005) Counting your customers, the easy way: An alternative to the Pareto/NBD model. Marketing Sci. 24(2):275–284.Link, Google Scholar
• Fang Y, Si L (2012) A latent pairwise preference learning approach for recommendation from implicit feedback. Proc. 21st ACM Internat. Conf. Inform. Knowledge Management (CIKM'12), October 29–November 2 (ACM, New York), 2567–2570.Crossref, Google Scholar
• Haubl G, Trifts V (2000) Consumer decision making in online shopping environments: The effects of interactive decision aids. Marketing Sci. 19(1):4–21.Link, Google Scholar
• Huang Z, Chung W, Chen H (2004) A graph model for e-commerce recommender systems. J. Amer. Soc. Inform. Sci. Tech. 55(3):259–274.Crossref, Google Scholar
• Kitts B, Freed D, Vrieze M (2000) Cross-sell: A fast promotion-tunable customer-item recommendation method based on conditionally independent probabilities. KDD'00: Proc. Sixth ACM SIGKDD Internat. Conf. Knowledge Discovery and Data Mining (ACM, New York), 437–446.Crossref, Google Scholar
• Konstan AJ, McNee MS, Ziegler C, Torres R, Kapoor N, Riedl JT (2006) Lessons on applying automated recommender systems to information-seeking tasks. Proc. 21st National Conf. Artificial Intelligence (AAAI, Boston), 1630–1633.Google Scholar
• Lenk PJ, DeSarbo WS, Green PE, Young MR (1996) Hierarchical Bayes conjoint analysis: Recovery of partworth heterogeneity from reduced experimental designs. Marketing Sci. 15(2):173–191.Link, Google Scholar
• Linden G (2008) People who read this article also read. IEEE Spectrum 45(3):46–60.Crossref, Google Scholar
• Louviere JJ, Woodworth G (1983) Design and analysis of simulated consumer choice or allocation experiments: An approach based on aggregate data. J. Marketing Res. 20:350–367.Crossref, Google Scholar
• Miller TW, Dickson PR (2001) On-line market research. Internat. J. Electronic Commerce 5(3):139–167.Crossref, Google Scholar
• Mobasher B, Berendt B, Spiliopoulou M (2001) KDD for personalization. Tutorial 5th Eur. Conf., Principles and Practice of Knowledge Discovery in Databases (PKDD-2001) (Springer-Verlag, Berlin).Google Scholar
• Montgomery AL (2001) Applying quantitative marketing techniques to the Internet. Interfaces 30(2):90–108.Link, Google Scholar
• Mookerjee R, Kumar S, Mookerjee V (2012) To show or not to show: Using user profiling to manage ad campaigns at Chitika. Interfaces 42(5):449–464.Link, Google Scholar
• Murthi BPS, Sarkar S (2003) The role of the management sciences in research on personalization. Management Sci. 49(10):1344–1362.Link, Google Scholar
• Padmanabhan B, Zheng Z, Kimbrough SO (2001) Personalization from incomplete data: What you don't know can hurt. ACM SIGKDD Internat. Conf. KDD (ACM, New York).Crossref, Google Scholar
• Peppers D, Rogers M (1997) Enterprise One to One: Tools for Competing in the Interactive Age (Doubleday, New York).Google Scholar
• Powell WB (2007) Approximate Dynamic Programming: Solving the Curses of Dimensionality (John Wiley & Sons, New York).Crossref, Google Scholar
• Reinartz WJ, Kumar V (2003) Customer lifetime duration: An empirical framework for measurement and explanation. J. Marketing 67(January):77–99.Crossref, Google Scholar
• Reinartz WJ, Thomas SJ, Kumar V (2005) Balancing acquisition and retention resources to maximize customer profitability. J. Marketing 69(1):63–79.Crossref, Google Scholar
• Rubens N, Kaplan D, Sugiyama M (2011) Active learning in recommender systems. Ricci F, Rokach L, Shapira B, Kantor PB, eds. Recommender Systems Handbook (Springer, New York), 735–767.Crossref, Google Scholar
• Sandvig JJ, Mobasher B, Burke R (2007) Robustness of collaborative recommendation based on association rule mining. Proc. 2007 Conf. Recommender Systems (ACM, New York), 105–112.Crossref, Google Scholar
• Schmittlein D, Morrison D, Colombo R (1987) Counting your customers: Who are they and what will they do next? Management Sci. 33(1):1–24.Link, Google Scholar
• Sethi SP, Thompson GL (2000) Optimal Control Theory: Applications to Management Science and Economics (Kluwer Academic Publishers, Norwell, MA).Google Scholar
• Shani G, Heckerman D, Brafman RI (2005) A MDP-based recommender system. J. Machine Learn. Res. 6:1265–1295.Google Scholar
• Sia KC, Zhu S, Chi Y, Hino K, Tseng BL (2007) Capturing user interests by both exploitation and exploration. Proc. 11th Internat. Conf. User Modeling 4511:334–339.Google Scholar
• Sutton RS (1990) Integrated architectures for learning, planning and reacting based on approximation dynamic programming. Proc. 7th Internat. Conf. Machine Learn. (Morgan Kaufmann, San Francisco), 216–224.Crossref, Google Scholar
• Tam YK, Ho YS (2005) Web personalization as a persuasion strategy: An elaboration likelihood model perspective. Inform. Systems Res. 16(3):271–291.Link, Google Scholar
• Toubier O, Hauser JR, Simester DJ (2004) Polyhedral methods for adaptive choice-based conjoint analysis. J. Marketing Res. 41:116–131.Crossref, Google Scholar
• Venkatesan R, Kumar V, Bohling T (2007) Optimal customer relationship management using Bayesian decision theory: An application for customer selection. J. Marketing Res. 44(4):579–594.Crossref, Google Scholar
• Vidale ML, Wolfe HB (1957) An operations-research study of sales response to advertising. Oper. Res. 5(3):370–381.Link, Google Scholar
• Wall Street Journal (2007) Firm mines offline data to target online ads. (October 17).Google Scholar
• Wei YZ, Moreau L, Jennings NR (2005) Learning users' interest by quality classification in market-based recommender systems. IEEE Trans. Knowledge Data Engrg. 17(12):1678–1688.Crossref, Google Scholar
• Zaiane OR (2002) Building a recommender agent for e-learning systems. Proc. Internat. Conf. Comput. Ed. (ICCE'02) (IEEE, Piscataway, NJ).Crossref, Google Scholar