Learning Personalized Product Recommendations with Customer Disengagement

Published Online:https://doi.org/10.1287/msom.2021.1047

References

  • Abbasi-Yadkori Y, Pál D, Szepesvári C (2011) Improved algorithms for linear stochastic bandits. NIPS'11: Proc. 24th Internat. Conf. Neural Inform. Processing Systems, 2312–2320.Google Scholar
  • Aflaki S, Popescu I (2013) Managing retention in service relationships. Management Sci. 60(2):415–433.LinkGoogle Scholar
  • Agrawal S, Goyal N (2013) Further optimal regret bounds for Thompson sampling. Artificial Intelligence Statist., 99–107.Google Scholar
  • Agrawal S, Avadhanula V, Goyal V, Zeevi A (2016) A near-optimal exploration-exploitation approach for assortment selection. Proc. 2016 ACM Conf. Econom. Comput. (ACM), 599–600.Google Scholar
  • Auer P (2002) Using confidence bounds for exploitation-exploration trade-offs. J. Machine Learn. Res. 3(Nov):397–422.Google Scholar
  • Bastani H (2021) Predicting with proxies: Transfer learning in high dimension. Management Sci. 67(5):2964–2984.LinkGoogle Scholar
  • Bastani H, Bayati M, Khosravi K (2021) Mostly exploration-free algorithms for contextual bandits. Management Sci. 67(3):1329–1349.LinkGoogle Scholar
  • Besbes O, Gur Y, Zeevi A (2015) Optimization in online content recommendation services: Beyond click-through rates. Manufacturing Service Oper. Management 18(1):15–33.LinkGoogle Scholar
  • Bowden JL-H (2009) The process of customer engagement: A conceptual framework. J. Marketing Theory Practice 17(1):63–74.CrossrefGoogle Scholar
  • Breese JS, Heckerman D, Kadie C (1998) Empirical analysis of predictive algorithms for collaborative filtering. UAI'98: Proc. Fourteenth Conf. Uncertainty Artificial Intelligence (Morgan Kaufmann Publishers Inc.), 43–52.Google Scholar
  • Bresler G, Chen GH, Shah D (2014) A latent source model for online collaborative filtering. NIPS, 3347–3355.Google Scholar
  • Chapelle O, Li L (2011) An empirical evaluation of Thompson sampling. NIPS'11: Proc. 24th Internat. Conf. Neural Inform. Processing Systems, 2249–2257.Google Scholar
  • Chen Y, Chi Y (2018) Harnessing structures in big data via guaranteed low-rank matrix estimation. Preprint, submitted May 2, https://arxiv.org/abs/1802.08397.Google Scholar
  • Chen X, Li Y, Mao J (2018) A nearly instance optimal algorithm for top-k ranking under the multinomial logit model. Proc. Twenty-Ninth Annual ACM-SIAM Sympos. Discrete Algorithms (SIAM), 2504–2522.Google Scholar
  • Davis MM, Vollmann TE (1990) A framework for relating waiting time and customer satisfaction in a service operation. J. Services Marketing 4(1):61–69.CrossrefGoogle Scholar
  • Demirezen EM, Kumar S (2016) Optimization of recommender systems based on inventory. Production Oper. Management 25(4):593–608.CrossrefGoogle Scholar
  • Farias VF, Li AA (2019) Learning preferences with side information. Management Sci. 65(7):3131–3149.LinkGoogle Scholar
  • Feng Y, Caldentey R, Ryan TC (2018) Robust learning of consumer preferences. Preprint, submitted August 7, http://dx.doi.org/10.2139/ssrn.3215614.Google Scholar
  • Ferreira K, Parthasarathy S, Sekar S (2019) Learning to rank an assortment of products. Preprint, submitted June 15, http://dx.doi.org/10.2139/ssrn.3395992.Google Scholar
  • Filippi S, Cappe O, Garivier A, Szepesvári C (2010) Parametric bandits: The generalized linear case. Adv. Neural Inform. Processing Systems 23 (NIPS 2010), 586–594.Google Scholar
  • Fitzsimons GJ, Lehmann DR (2004) Reactance to recommendations: When unsolicited advice yields contrary responses. Marketing Sci. 23(1):82–94.LinkGoogle Scholar
  • Garivier A, Kaufmann E (2016) Optimal best arm identification with fixed confidence. Conf. Learning Theory (PMLR), 998–1027.Google Scholar
  • Gittins J, Glazebrook K, Weber R (2011) Multi-Armed Bandit Allocation Indices (John Wiley & Sons, Chichester, United Kingdom).CrossrefGoogle Scholar
  • Gopalan A, Maillard O-A, Zaki M (2016) Low-rank bandits with latent mixtures. Preprint, submitted September 2016, https://arxiv.org/abs/1609.01508.Google Scholar
  • Harper FM, Konstan JA (2016) The MovieLens datasets: History and context. ACM Trans. Interactive Intelligent Systems 5(4):1–19.CrossrefGoogle Scholar
  • Herlocker JL, Konstan JA, Terveen LG, Riedl JT (2004) Evaluating collaborative filtering recommender systems. ACM Trans. Information Systems 22(1):5–53.CrossrefGoogle Scholar
  • Jain P, Dhillon IS (2013) Provable inductive matrix completion. Preprint, submitted June 4, https://arxiv.org/abs/1306.0626.Google Scholar
  • Johari R, Schmit S (2018) Learning with abandonment. Preprint, submitted February 23, https://arxiv.org/abs/1802.08718.Google Scholar
  • Johari R, Kamble V, Kanoria Y (2017) Matching while learning. Proc. 2017 ACM Conf. Econom. Comput. (ACM), 119.Google Scholar
  • Kahn BE (1995) Consumer variety-seeking among goods and services: An integrative review. J. Retailing Consumer Services 2(3):139–148.CrossrefGoogle Scholar
  • Kallus N, Udell M (2016) Dynamic assortment personalization in high dimensions. Preprint, submitted October 18, https://arxiv.org/abs/1610.05604.Google Scholar
  • Kanoria Y, Lobel I, Lu J (2018) Managing customer churn via service mode control. Preprint, submitted June 20, http://dx.doi.org/10.2139/ssrn.3188226.Google Scholar
  • Lai TL, Robbins H (1985) Asymptotically efficient adaptive allocation rules. Adv. Appl. Math. 6(1):4–22.CrossrefGoogle Scholar
  • Lattimore T, Szepesvari C (2016) The end of optimism? An asymptotic analysis of finite-armed linear bandits. Preprint, submitted October 14, https://arxiv.org/abs/1610.04491.Google Scholar
  • Li S, Karatzoglou A, Gentile C (2016) Collaborative filtering bandits. SIGIR (ACM), 539–548.Google Scholar
  • Li L, Jamieson K, DeSalvo G, Rostamizadeh A, Talwalkar A (2017) Hyperband: A novel bandit-based approach to hyperparameter optimization. J. Machine Learn. Res. 18(1):6765–6816.Google Scholar
  • Linden G, Smith B, York J (2003) Amazon.com recommendations: Item-to-item collaborative filtering. IEEE Internet Comput. 7(1):76–80.CrossrefGoogle Scholar
  • Lu Y, Musalem A, Olivares M, Schilkrut A (2013) Measuring the effect of queues on customer purchases. Management Sci. 59(8):1743–1763.LinkGoogle Scholar
  • Nerlove M, Arrow KJ (1962) Optimal advertising policy under dynamic conditions. Economica (New Series) 29(114):129–142.CrossrefGoogle Scholar
  • Russo D, Van Roy B (2014) Learning to optimize via posterior sampling. Math. Oper. Res. 39(4):1221–1243.LinkGoogle Scholar
  • Russo D, Van Roy B (2018) Satisficing in time-sensitive bandit learning. Preprint, submitted March 7, https://arxiv.org/abs/1803.02855.Google Scholar
  • Sarwar B, Karypis G, Konstan J, Riedl J (2001) Item-based collaborative filtering recommendation algorithms. WWW (ACM), 285–295.Google Scholar
  • Schein AI, Popescul A, Ungar LH, Pennock DM (2002) Methods and metrics for cold-start recommendations. SIGIR (ACM), 253–260.Google Scholar
  • Shah V, Blanchet J, Johari R (2018) Bandit learning with positive externalities. Preprint, submitted February 15, https://arxiv.org/abs/1802.05693.Google Scholar
  • Sousa R, Voss C (2012) The impacts of e-service quality on customer behaviour in multi-channel e-services. Total Quality Management Bus. Excellence 23(7-8):789–806.CrossrefGoogle Scholar
  • Su X, Khoshgoftaar TM (2009) A survey of collaborative filtering techniques. Adv. Artificial Intelligence 2009(2009):4.Google Scholar
  • Surprenant CF, Solomon MR (1987) Predictability and personalization in the service encounter. J. Marketing 51(2):86–96.CrossrefGoogle Scholar
  • Tan TF, Netessine S, Hitt L (2017) Is Tom Cruise threatened? an empirical study of the impact of product variety on demand concentration. Inform. Systems Res. 28(3):643–660.LinkGoogle Scholar
  • Venetis KA, Ghauri PN (2004) Service quality and customer retention: Building long-term relationships. Eur. J. Marketing 38(11/12):1577–1598.CrossrefGoogle Scholar
  • Welch BL (1951) On the comparison of several mean values: An alternative approach. Biometrika 38(3/4):330–336.CrossrefGoogle Scholar
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