Learning Product Rankings Robust to Fake Users
References
- (2015) The benefits of social influence in optimized cultural markets. PLoS One 10(4):e0121934.Crossref, Google Scholar
- (2016) Assortment optimization under a multinomial logit model with position bias and social influence. 4OR 14(1):57–75.Crossref, Google Scholar
- (2008) Sponsored search auctions with Markovian users. Papadimitriou CH, Zhang S, eds. Internet Proc. Network Econom., 4th Internat. Workshop, WINE, vol. 5385, Lecture Notes in Computer Science (Springer), 621–628.Google Scholar
- (2017) Thompson sampling for the MNL-bandit. Kale S, Shamir O, eds. Proc. 30th Conf. Learn. Theory COLT 2017, vol. 65 (PMLR), 76–78.Google Scholar
- (2013) Learning prices for repeated auctions with strategic buyers. Burges CJC, Bottou L, Ghahramani Z, Weinberger KQ, eds. Proc. Annual Conf. Adv. Neural Inform. Processing Systems 26: 27th Annual Conf. Neural Inform. Processing Systems, 1169–1177.Google Scholar
- (2014) Repeated contextual auctions with strategic buyers. Ghahramani Z, Welling M, Cortes C, Lawrence ND, Weinberger KQ, eds. Proc. Annual Conf. Adv. Neural Inform. Processing Systems, vol. 27, 622–630.Google Scholar
- (2020) Display optimization for vertically differentiated locations under multinomial logit preferences. Management Sci. 67(6):3519–3550.Link, Google Scholar
- (2020) Ranking an assortment of products via sequential submodular optimization. Preprint, submitted February 21, https://arxiv.org/abs/2002.09458.Google Scholar
- (2011) Position auctions with consumer search. Quart. J. Econom. 126(3):1213–1270.Crossref, Google Scholar
- (2002) Finite-time analysis of the multiarmed bandit problem. Machine Learn. 47(2–3):235–256.Crossref, Google Scholar
- (2021) MNL-bandit with knapsacks. Biró P, Chawla S, Echenique F, eds. EC 21: 22nd ACM Conf. Econom. Comput. (ACM, New York), 125–126.Google Scholar
- (2019) Contextual bandits with cross-learning. Wallach HM, Larochelle H, Beygelzimer A, d’Alché-Buc F, Fox EB, Garnett R, eds. Proc. Annual Conf. Adv. Neural Inform. Processing Systems, vol. 32, 9676–9685.Google Scholar
- (2020) Rethinking crowdfunding platform design: Mechanisms to deter misconduct and improve efficiency. Management Sci. 66(11):4980–4997.Link, Google Scholar
- (2014) Stochastic multi-armed-bandit problem with non-stationary rewards. Ghahramani Z, Welling M, Cortes C, Lawrence ND, Weinberger KQ, eds. Proc. Annual Conf. Adv. Neural Inform. Processing Systems, vol. 27, 199–207.Google Scholar
- (2015) Non-stationary stochastic optimization. Oper. Res. 63(5):1227–1244.Link, Google Scholar
- (2020) Robust algorithms for the secretary problem. Vidick T, ed. 11th Innovations Theoret. Comput. Sci. Conf. ITCS 2020, LIPIcs, vol. 151 (Schloss Dagstuhl—Leibniz-Zentrum für Informatik), 32:1–32:26.Google Scholar
- (2012) Regret analysis of stochastic and nonstochastic multi-armed bandit problems. Foundation Trends Machine Learn. 5(1):1–122.Google Scholar
- (2019) Sequential choice bandits: Learning with marketing fatigue. Preprint, submitted April 8, https://dx.doi.org/10.2139/ssrn.3355211.Google Scholar
- (2021) Seeding the herd: Pricing and welfare effects of social learning manipulation. Management Sci. 67(11):6734–6750.Link, Google Scholar
- (2017) A note on a tight lower bound for MNL-bandit assortment selection models. Preprint, submitted September 18, https://arxiv.org/abs/1709.06109.Google Scholar
- (2021) Revenue maximization and learning in products ranking. Bió P, Chawla S, Echenique F, eds. EC 21: 22nd ACM Conf. Econom. Comput. (ACM, New York), 316–317.Google Scholar
- (2019) Robust dynamic assortment optimization in the presence of outlier customers. Preprint, submitted October 9, https://arxiv.org/abs/1910.04183.Google Scholar
- (2022) Hedging the drift: Learning to optimize under non-stationarity. Management Sci. 68(3):1696–1713.Google Scholar
- (2020) Position ranking and auctions for online marketplaces. Management Sci. 66(8):3617–3634.Link, Google Scholar
- (2008) An experimental comparison of click position-bias models. Najork M, Broder AZ, Chakrabarti S, eds. Proc. Internat. Conf. Web Search Web Data Mining (ACM, New York), 87–94.Google Scholar
- (2013) Assortment planning under the multinomial logit model with totally unimodular constraint structures. Preprint, submitted April 10, https://people.orie.cornell.edu/jmd388/publications/MNLConstr.pdf.Google Scholar
- (2018) Product ranking on online platforms. Preprint, submitted March 6, https://dx.doi.org/10.2139/ssrn.3130378.Google Scholar
- (2018) Incentive-aware learning for large markets. Champin P-A, Gandon F, Lalmas M, Ipeirotis PG, eds. Proc. 2018 World Wide Web Conf. (ACM, New York), 1369–1378.Google Scholar
- (2015) Online allocation with traffic spikes: Mixing adversarial and stochastic models. Roughgarden T, Feldman M, Schwarz M, eds. Proc. 16th ACM Conf. Econom. Comput. (ACM, New York), 169–186.Google Scholar
- (2022) Learning to rank an assortment of products. Management Sci. 68(3):1828–1848.Google Scholar
- (2017) Amazon’s early Christmas bonus. The Wall Street Journal Online (November 28), https://www.wsj.com/articles/amazons-early-christmas-bonus-1511891465.Google Scholar
- (2020) Approximation algorithms for product framing and pricing. Oper. Res. 68(1):134–160.Link, Google Scholar
- (2018) Joint learning and optimization for multi-product pricing under a general cascade click model. Preprint, submitted November 5, https://dx.doi.org/10.2139/ssrn.3262808.Google Scholar
- (2011) The KL-UCB algorithm for bounded stochastic bandits and beyond. Kakade SM, von Luxburg U, eds. COLT-24th Annual Conf. Learn. Theory, vol. 19 (JMLR), 359–376.Google Scholar
- (2019) Incentive-aware contextual pricing with non-parametric market noise. Preprint, submitted November 8, https://arxiv.org/abs/1911.03508.Google Scholar
- S (2021) Dynamic incentive-aware learning: Robust pricing in contextual auctions. Oper. Res. 69(1):297–314.Google Scholar
- (2014) Real-time optimization of personalized assortments. Management Sci. 60(6):1532–1551.Link, Google Scholar
- (2019) Better algorithms for stochastic bandits with adversarial corruptions. Beygelzimer A, Hsu D, eds. Proc. Conf. Learn. Theory, vol. 99 (PMLR), 1562–1578.Google Scholar
- (2018) Online resource allocation under partially predictable demand. Preprint, submitted October 12, https://dx.doi.org/10.2139/ssrn.3252231.Google Scholar
- (2017) How can online marketplaces reduce rating manipulation? A new approach on dynamic aggregation of online ratings. Decision Support Systems 104:64–78.Crossref, Google Scholar
- (2022) To brush or not to brush: Product rankings, consumer search, and fake orders. Inform. Systems Research., ePub ahead of print May 20, https://doi.org/10.1287/isre.2022.1128.Google Scholar
- (2018) Adversarial attacks on stochastic bandits. Bengio S, Wallach H, Larochelle H, Grauman K, Cesa-Bianchi N, Garnett R, ed. Advances in Neural Information Processing Systems, vol. 31 (Curran Associates, Inc.), 3644–3653.Google Scholar
- (2020) Dynamic reserve prices for repeated auctions: Learning from bids. Preprint, submitted February 18, https://arxiv.org/abs/2002.07331.Google Scholar
- (2019) Corruption-tolerant bandit learning. Machine Learn. 108(4):687–715.Crossref, Google Scholar
- (2016) Multi-armed bandits: Competing with optimal sequences. Lee DD, Sugiyama M, von Luxburg U, Guyon I, Garnett R, eds. Proc. Annual Conf. Adv. Neural Inform. Processing Systems, vol. 29, 199–207.Google Scholar
- (2008) A cascade model for externalities in sponsored search. Papadimitriou CH, Zhang S, eds. Proc. Internet Network Econom., 4th Internat. Workshop WINE, Lecture Notes in Computer Science, vol. 5385 (Springer, New York), 585–596.Google Scholar
- (2020) Chasing demand: Learning and earning in a changing environment. Math. Oper. Res. 42(2):277–307.Link, Google Scholar
- (2015) Cascading bandits: Learning to rank in the cascade model. Bach FR, Blei DM, eds. Proc. 32nd Internat. Conf. Machine Learn., vol. 37, JMLR Workshop and Conference Proceedings Series (JMLR), 767–776.Google Scholar
- (2016) Multiple-play bandits in the position-based model. Lee DD, Sugiyama M, von Luxburg U, Guyon I, Garnett R, eds. Proc. Annual Conf. Neural Inform. Processing Systems, vol. 29, 1597–1605.Google Scholar
- (2020) Bandit Algorithms (Cambridge University Press, Cambridge, UK).Crossref, Google Scholar
- (2018) TopRank: A practical algorithm for online stochastic ranking. Bengio S, Wallach HM, Larochelle H, Grauman K, Cesa-Bianchi N, Garnett R, eds. Proc. 32nd Conf. Neural Inform. Processing Systems, 3949–3958.Google Scholar
- (2018) Randomized product display (ranking), pricing, and order fulfillment for e-commerce retailers. Preprint, submitted February 18, https://arxiv.org/abs/2002.07331.Google Scholar
- (2019) Cascading non-stationary bandits: Online learning to rank in the non-stationary cascade model. Kraus S, ed. Proc. 28th Internat. Joint Conf. Artificial Intelligence, 2859–2865.Google Scholar
- (2020) Action-manipulation attacks on stochastic bandits. IEEE Internat. Conf. Acoustics Speech Signal Processing (IEEE), 3112–3116.Google Scholar
- (2019) Data poisoning attacks on stochastic bandits. Chaudhuri K, Salakhutdinov R, eds. Proc. 36th Internat. Conf. Machine Learn., vol. 97 (PMLR), 4042–4050.Google Scholar
- (2016) Fake it till you make it: Reputation, competition, and Yelp review fraud. Management Sci. 62(12):3412–3427.Link, Google Scholar
- (2017) Efficient contextual bandits in non-stationary worlds. Preprint, submitted August 5, https://arxiv.org/abs/1708.01799.Google Scholar
- (2020) Bandits with adversarial scaling. Preprint, submitted March 4, https://arxiv.org/abs/2003.02287.Google Scholar
- (2018) Stochastic bandits robust to adversarial corruptions. Diakonikolas I, Kempe D, Henzinger M, eds. Proc. 50th Annual ACM SIGACT Sympos. Theory Comput. (ACM, New York), 114–122.Google Scholar
- (2019) Corruption robust exploration in episodic reinforcement learning. Preprint, submitted November 20, https://arxiv.org/abs/1911.08689.Google Scholar
- (2007) Allocating online advertisement space with unreliable estimates. MacKie-Mason JK, Parkes DC, Resnick P, eds. Proc. Eighth ACM Conf. Electronic Commerce (ACM, New York), 288–294.Google Scholar
- Maio N, Re B (2020) How Amazon’s e-commerce works? Internat. J. Tech. Bus. 2(1):8–13.Google Scholar
- (2018) Detecting bots and assessing their impact in social networks. Preprint, submitted October 29, https://arxiv.org/abs/1810.12398.Google Scholar
- (2020) Learning in combinatorial optimization: What and how to explore. Oper. Res. 68(5):1585–1604.Link, Google Scholar
- (2019) When is society susceptible to manipulation? Preprint, submitted March 2, 2020, https://dx.doi.org/10.2139/ssrn.3474643.Google Scholar
- (2019) Multi-product dynamic pricing with limited inventories under cascade click model. Preprint, submitted May 1, https://dx.doi.org/10.2139/ssrn.3362921.Google Scholar
- (2020) Online learning via offline greedy: Applications in market design and optimization. Preprint, submitted June 25, https://dx.doi.org/10.2139/ssrn.3613756.Google Scholar
- (2019) Thompson sampling for multinomial logit contextual bandits. Wallach HM, Larochelle H, Beygelzimer A, d’Alché-Buc F, Fox EB, Garnett R, eds. Proc. Annual Conf. Adv. Neural Inform. Processing Systems, vol. 32, 3145–3155.Google Scholar
- (2018) How sellers trick Amazon to boost sales. The Wall Street Journal Online (July 28), https://www.wsj.com/articles/how-sellers-trick-amazon-to-boost-sales-1532750493.Google Scholar
- (2018) The power of rankings: Quantifying the effect of rankings on online consumer search and purchase decisions. Marketing Sci. 37(4):530–552.Google Scholar
- (2007) Position auctions. Internat. J. Indust. Organ. 25(6):1163–1178.Google Scholar
- (2020) Making recommendations when users experience fatigue. Proc. Internat. Sympos. Artificial Intelligence Math. https://dblp.org/rec/conf/isaim/TulabandhulaW20.bib.Google Scholar
- (1979) Optimal search for the best alternative. Econometrica 47(3):641–654.Crossref, Google Scholar
- (2017) Online learning to rank in stochastic click models. Precup D, Whye Teh Y, eds. Proc. 34th Internat. Conf. Machine Learn., vol. 70 (PMLR), 4199–4208.Google Scholar

