Joint Learning and Optimization for Multi-Product Pricing (and Ranking) Under a General Cascade Click Model

References

• Agarwal A, Mukhopadhyay T (2016) The impact of competing ads on click performance in sponsored search. Inf. Syst. Res. 27(3):538–557.Link, Google Scholar
• Agrawal S, Avadhanula V, Goyal V, Zeevi A (2017). Thompson sampling for the mnl-bandit. Conference on Learning Theory, 76–78.Google Scholar
• Akcay Y, Natarajan H, Xu S (2010) Joint dynamic pricing of multiple perishable products under consumer choice. Manage. Sci. 56(8):1345–1361.Link, Google Scholar
• Ansari A, Mela CF (2003) E-customization. J. Mark. Res. 40(2):131–145.Crossref, Google Scholar
• Aouad A, Farias V, Levi R (2015) Assortment optimization under consider-then-choose choice models. Working Paper, MIT, Cambridge, MA.Google Scholar
• Aryafar K, Guillory D, Hong L (2017) An ensemble-based approachto click-through rate prediction for promoted listings at Etsy. Proceedings of the ADKDD’17, 1–6.Google Scholar
• Aslanyan G, Utkarsh P (2019) Position bias estimation for unbiased learning-to-rank in eCommerce search. Proc. SPIRE’19.Google Scholar
• Athey S, Ellison G (2011) Position auctions with consumer search. Q. J. Econom. 126(3):1213–1270.Crossref, Google Scholar
• Auer P, Ortner R (2007) Logarithmic online regret bounds for undiscounted reinforcement learning. Adv. Neural Inf. Process. Syst. 19:49–56.Google Scholar
• Aviv Y, Vulcano G (2012) Philips R, Ozer O, eds. Dynamic List Pricing. Pricing Management (Oxford University Press, Oxford, UK), 522–584.Crossref, Google Scholar
• Aydin G, Porteus EL (2008) Joint inventory and pricing decisions for an assortment. Oper. Res. 56(5):1247–1255.Link, Google Scholar
• Bitran G, Caldentey R (2003) An overview of pricing models for revenue management. Manufacturing Service Oper. Management 5(3):203–229.Link, Google Scholar
• Brafman RI, Tennenholtz M (2002) R-max a general polynomial time algorithm for near-optimal reinforcement learning. J. Machine Learn. Res. 3(Oct):213–231.Google Scholar
• Cao J, Sun W 2019. Dynamic learning of sequential choice bandit problem under marketing fatigue. 33rd AAAI Conf. Artificial Intelligence (AAAI-19).Google Scholar
• Chan TY, Park YH (2015) Consumer Search Activities and the Value of Ad Positions in Sponsored Search Advertising. Marketing Sci. 34(4):606–623.Link, Google Scholar
• Chen Q, Jasin S, Duenyas I (2016) Real-time dynamic pricing with minimal and flexible price adjustment. Management Sci. 62(8):2437–2455.Link, Google Scholar
• Chen Y, He C (2011) Paid placement: Advertising and search on the Internet. Econom J. 121(556):F309–F328.Google Scholar
• Chen M, Anderson J, Sohn M (2001) What can a mouse cursor tell us more? Correlation of eye/mouse movements on web browsing. Proc. CHI Extended Abstracts Human Factors in Comput. Systems, 281–282.Google Scholar
• Cheung WC, Simchi-Levi D (2017) Assortment optimization under unknown multinomial logit choice models. Working Paper, MIT, Cambridge, MA.Google Scholar
• Chuklin A, Markov I, Rijke M (2015) Click models for web search. Synth. Lect. Inf. Concepts Retr. Serv. 7(3):1–115.Crossref, Google Scholar
• Craswell N, Zoeter O, Taylor M, Ramsey B 2008. An experimental comparison of click position-bias models. Proc. 2008 Internat. Conf. Web Search Data Mining. ACM, 87–94.Google Scholar
• den Boer AV (2015) Dynamic pricing and learning: Historical origins, current research, and new directions. Surveys Oper. Res. Management Sci. 20(1):1–18.Crossref, Google Scholar
• Dong L, Kouvelis P, Tian Zh (2009) Dynamic pricing and inventory control of substitute products. Manufacturing Service Oper. Management 11(2):317–339.Link, Google Scholar
• Du Ch, Cooper WL, Wang Z (2016) Optimal pricing for a multinomial logit choice model with network effects. Oper. Res. 64(2):441–455.Link, Google Scholar
• Elmaghraby W, Keskinocak P (2003) Dynamic pricing in the presence of inventory considerations: Research overview, current practices, and future directions. Management Sci. 49(10):1287–1309.Link, Google Scholar
• Ferreira KJ, Simchi-Levi D, Wang H (2018) Online network revenue management using thompson sampling. Oper. Res. 66(6):1457–1759.Link, Google Scholar
• Gallego G, van Ryzin G (1997) A multiproduct dynamic pricing problem and its applications to network yield management. Oper. Res. 45(1):24–41.Link, Google Scholar
• Gao P, Ma Y, Chen N, Gallego G, Li A, Rusmevichientong P, Topaloglu H (2019) Assortment optimization and pricing under the multinomial logit model with impatient customers: sequential recommendation and selection. Oper. Res. 69(5):1349–1650Google Scholar
• Gastwirth JL (1976) On probabilistic models of consumer search for information. Q. J. Econom 90(1):38–50.Crossref, Google Scholar
• Ghose A, Ipeirotis PG, Li B (2014) Examining the impact of ranking on consumer behavior and search engine revenue. Management Sci. 60(7):1632–1654.Link, Google Scholar
• Ghose A, Yang S (2009) An empirical analysis of search engine advertising: Sponsored search in electronic markets. Management Sci. 55(10):1605–1622.Link, Google Scholar
• Granka L, Joachims T, Gay G (2004) Eye-tracking analysis of user behavior in WWW search. ACM Conf. Res. Development Inform. Retrieval (SIGIR).Google Scholar
• Guo F, Liu C, Wang YM (2009b) Efficient multiple-click models in web search. WSDM’09: Proc. 2nd ACM Internat. Conf. Web Search Data Mining, February 2009 (Association for Computing Machinery, location is New York), 124–131.Google Scholar
• Guo R, Zhao X, Henderson A, Hong L, Liu H (2020) Debiasing grid-based product search in e-commerce. KDD ’20 Proc. 26th ACM SIGKDD Internat. Conf. Knowledge Discoverry Data Mining, August 2020, (Association for Computing Machinery, New York), 2852–2860.Google Scholar
• Guo F, Liu C, Kannan A, Minka T, Taylor M, Wang Y, Faloutsos C (2009a) Click chain model in web search. Proc. 8th Internat. Conf. World Wide Web, 11–20.Google Scholar
• Hu M, Dang Ch, Chintagunta PK (2019) Search and learning at a Daily Deals website. Marketing Sci. 38(4):609–642.Link, Google Scholar
• Huang J, White RW, Buscher G (2012) User see, user point: Gaze and cursor alignment in web search. CHI’12: Proc. SIGCHI Conf. Human Factors Comput. Systems, May 2012, 1341–1350.Google Scholar
• Huang J, White RW, Dumais S (2011) No clicks, no problem: Using cursor movements to understand and improve search. Proc. CHI’11 (ACM), 1225–1234.Google Scholar
• Hauser JR (2014) Consideration-set heuristics. J. Bus. Res. 67(8):1688–1699.Crossref, Google Scholar
• Jagabathula S, Rusmevichientong P (2017) A nonparametric joint assortment and price choice model. Management Sci. 63(9):3128–3145.Link, Google Scholar
• Jaksch T, Ortner R, Auer P (2010) Near-optimal regret bounds for reinforcement learning. J. Mach. Learn. Res. 11(Apr):1563–1600.Google Scholar
• Jasin S (2014) Reoptimization and self-adjusting price control for network revenue management. Oper. Res. 62(5):1168–1178.Link, Google Scholar
• Joachims T, Granka L, Pan B, Hembrooke H, Gay G (2017) Accurately interpreting clickthrough data as implicit feedback. ACM SIGIR Forum 51, 4–11.Google Scholar
• Karmaker Santu SK, Sondhi P, Zhai C (2017) On application of learning to rank for E-commerce search. SIGIR ’17: Proc. 40th Internat. ACM SIGIR Conf. Res. Development, 475–484.Google Scholar
• Katariya S, Kveton B, Szepesvari C, Wen Z (2016) DCM bandits: Learning to rank with multiple clicks. Internat. Conf. Machine Learn., 1215–1224.Google Scholar
• Kearns M, Singh S (2002) Near-optimal reinforcement learning in polynomial time. Machine Learning 49(2-3):209–232.Crossref, Google Scholar
• Kempe D, Mahdian M (2008) A cascade model for externalities in sponsored search. Internet and Network Economics (Springer, Berlin-Heidelberg) 585–596.Crossref, Google Scholar
• Kveton B, Szepesvari C, Wen Z, Ashkan A (2015a) Cascading bandits: Learning to rank in the Cascade model. Proc. 32nd Internat. Conf. Machine Learn., vol. 37, 767–776.Google Scholar
• Kveton B, Wen Z, Ashkan A, Szepesvari C (2015b) Combinatorial cascading bandits. Proc. 28th Internat. Conf. Neural Inform. Processing Systems, vol. 1, 1450–1458.Google Scholar
• Lei Y, Jasin S, Sinha A (2018) Joint dynamic pricing and order fulfillment for e-commerce retailers. Manufacturing Service Oper. Management 20(2):269–284.Link, Google Scholar
• Levin Y, McGill J, Nediak M (2009) Dynamic pricing in the presence of strategic consumers and oligopolistic competition. Management Sci. 55(1):32–46.Link, Google Scholar
• Li P, Li R, Da Q, Zeng AX, Zhang L (2020) Improving Multi-Scenario Learning to Rank in E-commerce by Exploiting Task Relationships in the Label Space. CIKM ’20: Proc. 29th ACM Internat. Conf. Inform. Knowledge, 2605–2612.Google Scholar
• Liu Q, Arora N (2011) Efficient choice designs for a consider-then-choose model. Marketing Sci. 30(2):321–338.Link, Google Scholar
• Liu N, Ma Y, Topaloglu H (2020) Assortment Optimization under the Multinomial Logit Model with Sequential Offerings. INFORMS J. Comput. 32(3):835–853.Link, Google Scholar
• Liu Z, Mao J, Wang Ch, Ai Q, Liu Y, Nie J (2016) Enhancing click models with mouse movement information. Inform. Retrieval J. 20:53–80.Crossref, Google Scholar
• McCall JJ (1970) Economics of information and job search. Quart. J. Econom. 84(1):113–126.Crossref, Google Scholar
• Najafi S, Duenyas I, Jasin S, Uichanco J (2019) Multi-product dynamic pricing with limited inventories under Cascade click model. Working Paper, HEC Paris, Jouyen-Josas, France.Google Scholar
• Osband I, Van Roy B (2014) Near-optimal reinforcement learning in factored MDPs. Adv. Neural Inf. Process. Syst. 27:604–612.Google Scholar
• Phillips R (2005) Pricing and Revenue Optimization. (Business Books, Stanford, CA).Crossref, Google Scholar
• Richardson ME, Dominowska R, Ragno R (2007) Predicting clicks: estimating the click-through rate for new ads. Proc. 16th Internat. Conf. World Wide Web, 521–530.Google Scholar
• Rodden K, Xin F (2007) Exploring how mouse movements relate to eye movements on web search results pages. Proc. ACM SIGIR 2007 Workshop Web Inform. Seeking Interaction, 29–32.Google Scholar
• Russo D, Van Roy B (2014) Learning to optimize via posterior sampling. Math. Oper. Res. 39(4):1221–1243.Link, Google Scholar
• Saito Y (2020) Doubly robust estimator for ranking metrics with post-click conversions. RecSys ’20: 14th ACM Conf. Recommender Systems, September 2020, 92–100.Google Scholar
• Simon HA (1955) A behavioral model of rational choice. Q. J. Econom. 69(1):99–118.Crossref, Google Scholar
• Sims ChA (2003) Implications of rational inattention. J. Monet. Econ. 50(3):665–690.Crossref, Google Scholar
• Wagner L, Martínez-de-Albéniz V (2020) Pricing and assortment strategies with product exchanges. Oper. Res. 68(2):453–466.Abstract, Google Scholar
• Wang Q, Chen W (2017) Improving regret bounds for combinatorial semi-bandits with probabilistically triggered arms and its applications. Preprint, submitted March 5, https://arxiv.org/abs/1703.01610.Google Scholar
• Wang R, Sahin O (2018) The impact of consumer search cost on assortment planning and pricing. Management Sci. 64(8):3649–3666.Link, Google Scholar
• Weitzman ML (1979) Optimal search for the best alternative. Econometrica. 47(3):641–654.Crossref, Google Scholar
• Wu L, Hu D, Hong L, Liu H (2018) Turning clicks into purchases: Revenue optimization for product search in e-commerce. SIGIR ’18: 41st Internat. ACM SIGIR Conference on Research Development Inform. Retrieval, June 2018, 365–374.Google Scholar
• Yu X, Ma H, Hsu BJ, Han J (2014) On building entity recommendersystems using user click Log and freebase knowledge. WSDM ’14:Proc. 7th ACM Internat. Conf. Web Search Data Mining, 263–272.Google Scholar
• Yang N, Zhang R (2014) Dynamic pricing and inventory management under inventory-dependent demand. Oper. Res. 62(5):1077–1094.Link, Google Scholar
• Zhang H, Jasin S (2021) Online learning and optimization of (some) cyclic pricing policies for revenue management with patient customers. Manufacturing Service Oper. Management, ePub ahead of print October 26, https://pubsonline.informs.org/doi/10.1287/msom.2021.0979.Link, Google Scholar
• Zhu ZA, Chen W, Minka T, Zhu Ch, Chen Zh (2010) A novel click model and its applications to online advertising. Proc. 3rd ACM Internat. Conf. Web Search Data Mining, 321–330.Google Scholar
• Zoghi M, Tunys T, Ghavamzadeh M, Kveton B, Szepesvari C, Wen Z (2017) Online learning to rank in stochastic click models. Internat. Conf. Machine Learn., 4199–4208.Google Scholar
• Zong S, Ni H, Sung K, Ke NR, Wen Z, Kveton B (2016) Cascading bandits for large-scale recommendation problems. Proc. 32nd Conf. Uncertainty Artificial Intelligence, 835–844.Google Scholar