Recommending Products When Consumers Learn Their Preference Weights

Published Online:https://doi.org/10.1287/mksc.2018.1144

References

  • Adam K (2001) Learning while searching for the best alternative. J. Econom. Theory 101(1):252–280.CrossrefGoogle Scholar
  • Adamopoulos P, Tuzhilin A (2014) On unexpectedness in recommender systems: or how to better expect the unexpected. ACM Trans. Intelligent Systems Tech. 5(4):54:1–32.Google Scholar
  • Adomavičius G, Tuzhilin A (2005) Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions. IEEE Trans. Knowledge Data Engrg. 17(6):734–749.CrossrefGoogle Scholar
  • Ansari A, Essegaier S, Kohli R (2000) Internet recommendation systems. J. Marketing Res. 37(3):363–376.CrossrefGoogle Scholar
  • Bikhchandani S, Sharma S (1996) Optimal search with learning. J. Econom. Dynam. Control 20(1):333–359.CrossrefGoogle Scholar
  • Bodapati A (2008) Recommendation systems with purchase data. J. Marketing Res. 45(1):77–93.CrossrefGoogle Scholar
  • Branco F, Sun M, Villas-Boas JM (2012) Optimal search for product information. Management Sci. 58(11):2037–2056.LinkGoogle Scholar
  • Bronnenberg BJ, Kim J, Mela CF (2016) Zooming in on choice: How do consumers search for cameras online? Marketing Sci. 35(5):693–712.Google Scholar
  • Castells P, Vargas S, Wang J (2011) Novelty and diversity metrics for recommender systems: choice, discovery and relevance. Macdonald C, Wang J, Clarke C, eds. Proc. Internat. Workshop Diversity Document Retrieval, Dublin, Ireland.Google Scholar
  • Celma Ò, Herrera P (2008) A new approach to evaluating novel recommendations. Proc. 2008 ACM Conf. Recommender Systems (ACM, New York), 179–186.Google Scholar
  • Chen Y, Yao S (2016) Sequential search with refinement: Model and application with clickstream data. Management Sci. 63(12):4345–4365.LinkGoogle Scholar
  • Chick SE, Frazier P (2012) Sequential sampling with economics of selection procedures. Management Sci. 58(3):550–569.LinkGoogle Scholar
  • Chung J, Rao VR (2012) A general consumer preference model for experience products: Application to internet recommendation services. J. Marketing Res. 49(3):289–305.CrossrefGoogle Scholar
  • Cook J (2012) MiniDates schedules real-life (legitimately) blind dates for you. TechCrunch (May 30), https://techcrunch.com/2012/05/30/minidates-schedules-real-life-legitimately-blind-dates-for-you/.Google Scholar
  • De Bruyn A, Liechty JC, Huizingh EKRE, Lilien GL (2008) Offering online recommendations with minimum customer input through conjoint-based decision aids. Marketing Sci. 27(3):443–460.LinkGoogle Scholar
  • Dzyabura D, Jagabathula S (2018) Offline assortment optimization in the presence of an online channel. Management Sci. 64(6):2767–2786.Google Scholar
  • Dzyabura D, Jagabathula S, Muller E (2019) Accounting for discrepancies between online and offline product evaluations. Marketing Sci. 38(1):88–106.Google Scholar
  • Finkel EJ, Eastwick PW, Karney BR, Reis HT, Sprecher S (2012) Online dating: A critical analysis from the perspective of psychological science. Psych. Sci. Public Interest 13(1):3–66.CrossrefGoogle Scholar
  • Fleder D, Hosanagar K (2009) Blockbuster culture’s next rise or fall: The impact of recommender systems on sales diversity. Management Sci. 55(5):697–712.LinkGoogle Scholar
  • Ge M, Delgado-Battenfeld C, Jannach D (2010) Beyond accuracy: Evaluating recommender systems by coverage and serendipity. Proc. 2008 ACM Conf. Recommender Systems (ACM, New York), 257–260.Google Scholar
  • Ghose A, Ipeirotis PG, Li B (2012) Designing ranking systems for hotels on travel search engines by mining user-generated and crowdsourced content. Marketing Sci. 31(3):493–520.LinkGoogle Scholar
  • Gittins JC (1979) Bandit processes and dynamic allocation indices. J. Roy. Statist. Soc. Ser. B (Methodological) 41(2):148–177.CrossrefGoogle Scholar
  • Gittins JC, Glazebrook K, Weber R (2011) Multi-armed Bandit Allocation Indices (John Wiley & Sons, London).CrossrefGoogle Scholar
  • Greenleaf EA, Lehmann DR (1995) Reasons for substantial delay in consumer decision making. J. Consumer Res. 22(2):186–199.CrossrefGoogle Scholar
  • Häubl G, Trifts V (2000) Consumer decision making in online shopping environments: The effects of interactive decision aids. Marketing Sci. 19(1):4–21.LinkGoogle Scholar
  • Hauser JR, Dong S, Ding M (2014a) Self-reflection and articulated consumer preferences. J. Product Innovation Management 31(1):17–32.CrossrefGoogle Scholar
  • Hauser JR, Liberali G, Urban GL (2014b) Website morphing 2.0: Switching costs, partial exposure, random exit, and when to morph. Management Sci. 60(6):1594–1616.LinkGoogle Scholar
  • Hauser JR, Urban GL, Liberali G, Braun M (2009) Website morphing. Marketing Sci. 28(2):202–224.LinkGoogle Scholar
  • Herlocker J, Konstan J, Terveen L, Riedl J (2004) Evaluating collaborative filtering recommender systems. ACM Trans. Inform. Systems 22(1):5–53.CrossrefGoogle Scholar
  • Hong H, Shum M (2006) Using price distributions to estimate search costs. RAND J. Econom. 37(2):257–275.CrossrefGoogle Scholar
  • Honka E (2014) Quantifying search and switching costs in the US auto insurance industry. RAND J. Econom. 45(4):847–884.CrossrefGoogle Scholar
  • Jacobs BJD, Donkers B, Fok D (2016) Model-based purchase predictions for large assortments. Marketing Sci. 35(3):389–404.LinkGoogle Scholar
  • Jun T (2004) A survey on the bandit problem with switching costs. Economist 152(4):513–541.CrossrefGoogle Scholar
  • Ke TT, Shen Z-JM, Villas-Boas, JM (2016) Search for information on multiple products. Management Sci. 62(12):3576–3603.Google Scholar
  • Kim JB, Albuquerque P, Bronnenberg BJ (2010) Online demand under limited consumer search. Marketing Sci. 29(6):1001–1023.LinkGoogle Scholar
  • Lin S, Zhang J, Hauser JR (2014) Learning from experience, simply. Marketing Sci. 34(1):1–19.LinkGoogle Scholar
  • Liu NN, Zhao M, Xiang E, Yang Q (2010) Online evolutionary collaborative filtering. Proc. 4th ACM Conf. Recommender Systems (ACM, New York), 95–102.CrossrefGoogle Scholar
  • Lu S, Xiao L, Ding M (2016) A video-based automated recommender (VAR) system for garments. Marketing Sci. 35(3):484–510.LinkGoogle Scholar
  • McNee S, Riedl J, Konstan J (2006) Accurate is not always good: How accuracy metrics have hurt recommender systems. Extended Abstracts on ACM Human Factors in Computing Systems (ACM, New York), 1097–1101.CrossrefGoogle Scholar
  • Mersereau AJ, Rusmevichientong P, Tsitsiklis JN (2009) A structured multiarmed bandit problem and the greedy policy. IEEE Trans. Automatic Control 54(12):2787–2802.CrossrefGoogle Scholar
  • Misra K, Schwartz EM, Abernethy J (2017) Dynamic online pricing with incomplete information using multi-armed bandit experiments. Working paper, University of Michigan, Ann Arbor.Google Scholar
  • Moon S, Russell GL (2008) Predicting product purchase from inferred customer similarity: An autologistic model approach. Management Sci. 54(1):71–82.LinkGoogle Scholar
  • Rogers A (2013) After you read the listings, your agent reads you. New York Times (March 26), F4.Google Scholar
  • Schwartz EM, Bradlow ET, Fader PS (2017) Customer acquisition via display advertising using multi-armed bandit experiments. Marketing Sci. 36(4):500–522.LinkGoogle Scholar
  • Seiler S (2013) The impact of search costs on consumer behavior: A dynamic approach. Quant. Marketing Econom. 11(2):155–203.CrossrefGoogle Scholar
  • She J, MacDonald EF (2013) Trigger features on prototypes increase preference for sustainability. Proc. 25th ASME Internat. Conf. Design Theory Methodology, Portland, OR.CrossrefGoogle Scholar
  • Sheehy K (2013) Study: High school grads choosing wrong college majors. U.S. News World Rep. (November 11). http://www.usnews.com/education/blogs/high-school-notes/2013/11/11/study-high-school-grads-choosing-wrong-college-majors.Google Scholar
  • Tversky A (1972) Elimination by aspects: A theory of choice. Psych. Rev. 79(4):281–299.CrossrefGoogle Scholar
  • Urban GL, Hauser JR (2004) ‘Listening-in’ to find and explore new combinations of customer needs. J. Marketing 68(2):72–87.CrossrefGoogle Scholar
  • Vargas S, Castells P (2011) Rank and relevance in novelty and diversity metrics for recommender systems. Proc. 5th ACM Conf. Recommender Systems (ACM, New York), 109–116.Google Scholar
  • Weitzman ML (1979) Optimal search for the best alternative. Econometrica 47(3):641–654.CrossrefGoogle Scholar
  • Whittle P (1988) Restless bandits: Activity allocation in a changing world. J. Appl. Probab. 25(A):287–298.CrossrefGoogle Scholar
  • Ying Y, Feinberg F, Wedel M (2006) Leveraging missing ratings to improve online recommendation systems. J. Marketing Res. 43(3):355–365.CrossrefGoogle Scholar
  • Zhang M, Hurley N (2008) Avoiding monotony: Improving the diversity of recommendation lists. Proc. 2008 ACM Conf. Recommender Systems (ACM, New York), 123–130.Google Scholar
  • Zhou T, Kuscsik Z, Liu J-G, Medo M, Wakeling JR, Zhang Y-C (2010) Solving the apparent diversity-accuracy dilemma of recommender systems. Proc. Natl. Acad. Sci. 107(10):4511–4515.CrossrefGoogle Scholar
  • Ziegler C-N, McNee SM, Konstan JA, Lausen G (2005) Improving recommendation lists through topic diversification. ACM Proc. 14th Internat. Conf. World Wide Web (ACM, New York), 22–32.CrossrefGoogle Scholar
INFORMS site uses cookies to store information on your computer. Some are essential to make our site work; Others help us improve the user experience. By using this site, you consent to the placement of these cookies. Please read our Privacy Statement to learn more.