Robust Learning of Consumer Preferences

References

• Agrawal S, Avadhanula V, Goyal V, Zeevi A (2017) Thompson sampling for the MNL-bandit. Kale S, Shamir O, eds. Proc. 2017 Conf. Learn. Theory (JMLR.org), 76–78.Google Scholar
• Agrawal S, Avadhanula V, Goyal V, Zeevi A (2019) MNL-bandit: A dynamic learning approach to assortment selection. Oper. Res. 67(5):1453–1485.Link, Google Scholar
• Ailon N (2012) An active learning algorithm for ranking from pairwise preferences with an almost optimal query complexity. J. Machine Learn. Res. 13(5):137–164.Google Scholar
• Ailon N, Charikar M, Newman A (2008) Aggregating inconsistent information: Ranking and clustering. J. ACM 55(5):1–27.Crossref, Google Scholar
• Ali A, Meila M (2012) Experiments with Kemeny ranking: What works when? Math. Social Sci. 64(1):28–40.Crossref, Google Scholar
• Alon N (2006) Ranking tournaments. SIAM J. Discrete Math. 20(1):137–142.Crossref, Google Scholar
• Araman V, Caldentey R (2016) Crowdvoting the timing of new product introduction. Preprint, submitted January 27, https://dx.doi.org/10.2139/ssrn.2723515.Google Scholar
• Audibert JY, Bubeck S (2010) Best arm identification in multi-armed bandits. Kalai AT and Mohri M, eds. Proc. 23rd Internat. Conf. Learn. Theory (JMLR.org), 41–53.Google Scholar
• Betabrand (2021) Accessed October 20, 2021, https://www.betabrand.com/.Google Scholar
• Brabham CD (2010) Moving the crowd at Threadless. Inform. Comm. Soc. 13(8):1122–1145.Crossref, Google Scholar
• Braverman M, Mossel E (2008) Noisy sorting without resampling. Teng SH, ed. Proc. 19th Annual ACM-SIAM Sympos. Discrete Algorithms (Society for Industrial and Applied Mathematics, Philadelphia), 268–276.Google Scholar
• Braverman M, Mossel E (2009) Sorting from noisy information. Preprint, submitted October 7, https://arxiv.org/abs/0910.1191.Google Scholar
• Bubeck S, Munos R, Stoltz G (2011) Pure exploration in finitely-armed and continuous-armed bandits. Theoretical Comput. Sci. 412(19):1832–1852.Crossref, Google Scholar
• Caragiannis I, Procaccia AD, Shah N (2013) When do noisy votes reveal the truth? McAfee P, Tardos E, eds. Proc. 14th ACM Conf. Electronic Commerce (Association for Computing Machinery, New York), 143–160.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
• Charbit P, Thomassé S, Yeo A (2007) The minimum feedback arc set problem is NP-hard for tournaments. Combin. Probab. Comput. 16(1):1–4.Crossref, Google Scholar
• Charon I, Hudry O (2010) An updated survey on the linear ordering problem for weighted or unweighted tournaments. Ann. Oper. Res. 175(1):107–158.Crossref, Google Scholar
• Chen X, Wang Y (2018) A note on tight lower bound for capacitated MNL-bandit assortment selection models. Oper. Res. Lett. 46(5):534–537.Google Scholar
• Chen X, Li Y, Mao J (2018) A nearly instance optimal algorithm for top-k ranking under the multinomial logit model. Czumaj A, ed. Proc. 29th Annual ACM-SIAM Symposium on Discrete Algorithms (Society for Industrial and Applied Mathematics, Philadelphia), 2504–2522.Google Scholar
• Chernoff H (1959) Sequential design of experiments. Ann. Math. Statist. 30(3):755–770.Crossref, Google Scholar
• Chernoff H (1972) Sequential Analysis and Optimal Design (SIAM, Philadelphia).Crossref, Google Scholar
• Chung F, Lu L (2006) Concentration inequalities and martingale inequalities: A survey. Internet Math. 3(1):79–127.Crossref, Google Scholar
• Conitzer V, Davenport A, Kalagnanam J (2006) Improved bounds for computing Kemeny rankings. Cohn A, ed. Proc. 21st Natl. Conf. Artificial Intelligence (AAAI Press, Palo Alto, CA), 620–626.Google Scholar
• Dantzig G (1963) Linear Programming and Extensions (Princeton University Press, Princeton, NJ).Crossref, Google Scholar
• Davenport A, Kalagnanam J (2004) A computational study of the Kemeny rule for preference aggregation. Cohn AG, ed. Proc. 19th Natl. Conf. Artificial Intelligence (AAAI Press, Palo Alto, CA), 697–702.Google Scholar
• Désir A, Goyal V, Jagabathula S, Segev D (2021) Mallows-smoothed distribution over rankings approach for modeling choice. Oper. Res. 69(4):1206–1227.Google Scholar
• Draglia V, Tartakovsky AG, Veeravalli VV (1999) Multihypothesis sequential probability ratio tests. I. Asymptotic optimality. IEEE Trans. Inform. Theory 45(7):2448–2461.Crossref, Google Scholar
• Falahatgar M, Orlitsky A, Pichapati V, Suresh AT (2017) Maximum selection and ranking under noisy comparisons. Precup D, Teh YW, eds. Proc. 34th Internat. Conf. Machine Learn. (JMLR.org), 1088–1096.Google Scholar
• Fomin FV, Lokshtanov D, Raman V, Saurabh S (2010) Fast local search algorithm for weighted feedback arc set in tournaments. Fox M, Poole D, eds. Proc. 24th AAAI Conf. Artificial Intelligence (AAAI Press, Palo Alto, CA), 65–70.Google Scholar
• Gabillon V, Ghavamzadeh M, Lazaric A (2012) Best arm identification: A unified approach to fixed budget and fixed confidence. Pereira F, Burges CJC, Bottou L, Weinberger KQ, eds. Proc. 25th Internat. Conf. Neural Inform. Processing Systems (Curran Associates Inc., Red Hook, NY), 3212–3220.Google Scholar
• Garivier A, Kaufmann E (2016) Optimal best arm identification with fixed confidence. Feldman V, Rakhlin A, Shamir O, eds. Proc. 29th Annual Conf. Learn. Theory (JMLR.org), 998–1027.Google Scholar
• Grötschel M, Jünger M, Reinelt G (1984) A cutting plane algorithm for the linear ordering problem. Oper. Res. 32(6):1195–1220.Link, Google Scholar
• Heckel R, Shah NB, Ramchandran K, Wainwright MJ (2019) Active ranking from pairwise comparisons and when parametric assumptions do not help. Ann. Statist. 47(6):3099–3126.Crossref, Google Scholar
• Huang Y, Singh VP, Srinivasan K (2014) Crowdsourcing new product ideas under consumer learning. Management Sci. 60(9):2138–2159.Link, Google Scholar
• Jamieson KG, Katariya S, Deshpande A, Nowak RD (2015) Sparse dueling bandits. Lebanon G, Vishwanathan SVN, eds. Proc. 18th Internat. Conf. Artificial Intelligence and Statist. (JMLR.org), 416–424.Google Scholar
• Jiang X, Lim LH, Yao Y, Ye Y (2011) Statistical ranking and combinatorial Hodge theory. Math. Programming 127(1):203–244.Crossref, Google Scholar
• Kaufmann E, Cappé O, Garivier A (2016) On the complexity of best arm identification in multi-armed bandit models. J. Machine Learn. Res. 17(1):1–42.Google Scholar
• Kenyon-Mathieu C, Schudy W (2007) How to rank with few errors. Feige U, ed. Proc. 39th Annual ACM Sympos. Theory Comput. (Association for Computing Machinery, New York), 95–103.Google Scholar
• King A, Lakhani KR (2013) Using open innovation to identify the best ideas. MIT Sloan Management Rev. 55(1):41–48.Google Scholar
• Lego (2018) Accessed October 20, 2021, https://ideas.lego.com/.Google Scholar
• Levin J, Nalebuff B (1995) An introduction to vote-counting schemes. J. Econom. Perspect. 9(1):3–26.Crossref, Google Scholar
• Li X, Liu J, Ying Z (2014) Generalized sequential probability ratio test for separate families of hypotheses. Sequential Anal. 33(4):539–563.Crossref, Google Scholar
• Marinesi S, Girotra K (2012) Information acquisition through customer voting systems. Preprint, submitted December 21, https://dx.doi.org/10.2139/ssrn.2191940.Google Scholar
• Mitchell JE, Borchers B (1996) Solving real-world linear ordering problems using a primal-dual interior point cutting plane method. Ann. Oper. Res. 62(1):253–276.Crossref, Google Scholar
• Naghshvar M, Javidi T (2013) Active sequential hypothesis testing. Ann. Statist. 41(6):2703–2738.Crossref, Google Scholar
• Pee LG (2016) Customer co-creation in B2C e-commerce: Does it lead to better new products? Electronic Commerce Res. 16(2):217–243.Crossref, Google Scholar
• PREFLIB (2021) Accessed October 21, 2021, http://www.preflib.org/.Google Scholar
• Raykar CV, Yu S, Zhao HL, Valadez HG, Florin C, Bogoni L, Moy L (2010) Learning from crowds. J. Machine Learn. Res. 11(43):1297–1322.Google Scholar
• Rusmevichientong P, Shen ZJM, Shmoys DB (2010) Dynamic assortment optimization with a multinomial logit choice model and capacity constraint. Oper. Res. 58(6):1666–1680.Link, Google Scholar
• Russo D (2020) Simple Bayesian algorithms for best arm identification. Oper. Res. 68(6):1625–1647.Google Scholar
• Sauré D, Zeevi A (2013) Optimal dynamic assortment planning with demand learning. Manufacturing Service Oper. Management 15(3):387–404.Link, Google Scholar
• Schalekamp F, van Zuylen A (2009) Rank aggregation: Together we’re strong. Finocchi I, Hershberger J, eds. Proc. Workshop Algorithm Engrg. Experiments (Society for Industrial and Applied Mathematics, Philadelphia), 38–51.Google Scholar
• Schneider J, Hall J (2011) Why most product launches fail. Harvard Bus. Rev. 89(4):21–23.Google Scholar
• Shah NB, Wainwright MJ (2017) Simple, robust and optimal ranking from pairwise comparisons. J. Machine Learn. Res. 18(1):7246–7283.Google Scholar
• Szörényi B, Busa-Fekete R, Paul A, Hüllermeier E (2015) Online rank elicitation for Plackett-Luce: A dueling bandits approach. Cortes C, Lee DD, Sugiyama M, Garnett R, eds. Proc. 28th Internat. Conf. Neural Inform. Processing Systems (MIT Press, Cambridge, MA), 604–612.Google Scholar
• Ulu C, Honhon D, Alptekinoglu A (2012) Learning consumer tastes through dynamic assortments. Oper. Res. 60(4):833–849.Link, Google Scholar
• van Zuylen A, Williamson DP (2009) Deterministic pivoting algorithms for constrained ranking and clustering problems. Math. Oper. Res. 34(3):594–620.Link, Google Scholar
• Vinayak RK, Hassibi B (2016) Crowdsourced clustering: Querying edges vs triangles. Lee DD, von Luxburg U, Garnett R, Sugiyama M, Guyon I, eds. Proc. 30th Internat. Conf. Neural Inform. Processing Systems (Curran Associates Inc., Red Hook, NY), 1316–1324.Google Scholar
• Wald A (1973) Sequential Analysis (Courier Corporation, Dover Publications Inc., Mineola, New York).Google Scholar
• Wauthier FL, Jordan MI, Jojic N (2013) Efficient ranking from pairwise comparisons. Dasgupta S, McAllester D, eds. Proc. 30th Internat. Conf. Machine Learn. (JMLR.org), 109–117.Google Scholar
• Young HP (1988) Condorcet’s theory of voting. Amer. Political Sci. Rev. 82(4):1231–1244.Crossref, Google Scholar