Sequential Search Transformer: A Deep Structural Econometric Model

References

• Adomavicius 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.Crossref, Google Scholar
• Andreasen AR (1984) Life status changes and changes in consumer preferences and satisfaction. J. Consumer Res. 11(3):784–794.Crossref, Google Scholar
• Bettman JR, Luce MF, Payne JW (1998) Constructive consumer choice processes. J. Consumer Res. 25(3):187–217.Crossref, Google Scholar
• Bishop CM (2006) Pattern Recognition and Machine Learning (Information Science and Statistics) (Springer-Verlag, Berlin).Google Scholar
• Bishop CM, Bishop H (2023) Deep Learning: Foundations and Concepts (Springer Nature, Berlin).Google Scholar
• Bronnenberg BJ, Dubé JPH, Gentzkow M (2012) The evolution of brand preferences: Evidence from consumer migration. Amer. Econom. Rev. 102(6):2472–2508.Crossref, Google Scholar
• Camilleri AR (2017) The presentation format of review score information influences consumer preferences through the attribution of outlier reviews. J. Interactive Marketing 39(1):1–14.Crossref, Google Scholar
• Chen Y, Yao S (2017) Sequential search with refinement: Model and application with click-stream data. Management Sci. 63(12):4345–4365.Link, Google Scholar
• Du M, Liu N, Hu X (2019) Techniques for interpretable machine learning. Comm. ACM 63(1):68–77.Crossref, Google Scholar
• Ghose A, Ipeirotis PG, Li B (2019) Modeling consumer footprints on search engines: An interplay with social media. Management Sci. 65(3):1363–1385.Link, Google Scholar
• Glorot X, Bordes A, Bengio Y (2011) Deep sparse rectifier neural networks. Gordon GJ, Dunson DB, Dudík M, eds. Proc. 14th Internat. Conf. Artificial Intelligence Statist. (AISTATS 2011), vol. 15 (JMLR.org, Fort Lauderdale, FL), 315–323.Google Scholar
• Gu Y, Ding Z, Wang S, Zou L, Liu Y, Yin D (2020) Deep multifaceted transformers for multi-objective ranking in large-scale e-commerce recommender systems. Jagadish HV, Silvestri F, eds. Proc. 29th ACM Internat. Conf. Inform. Knowledge Management (ACM, New York), 2493–2500.Google Scholar
• Hidasi B, Karatzoglou A, Baltrunas L, Tikk D (2015) Session-based recommendations with recurrent neural networks. Preprint, submitted November 21, https://arxiv.org/abs/1511.06939.Google Scholar
• Honka E (2014) Quantifying search and switching costs in the us auto insurance industry. RAND J. Econom. 45(4):847–884.Crossref, Google Scholar
• Honka E, Chintagunta P (2017) Simultaneous or sequential? Search strategies in the US auto insurance industry. Marketing Sci. 36(1):21–42.Link, Google Scholar
• Iyengar SS, Lepper MR (2000) When choice is demotivating: Can one desire too much of a good thing? J. Personality Soc. Psych. 79(6):995. Crossref, Google Scholar
• Jiang Z, Chan T, Che H, Wang Y (2021) Consumer search and purchase: An empirical investigation of retargeting based on consumer online behaviors. Marketing Sci. 40(2):219–240.Link, Google Scholar
• Kaji T, Manresa E, Pouliot G (2023) An adversarial approach to structural estimation. Econometrica 91(6):2041–2063.Crossref, Google Scholar
• Kim JB, Albuquerque P, Bronnenberg BJ (2010) Online demand under limited consumer search. Marketing Sci. 29(6):1001–1023.Link, Google Scholar
• Kopalle P (2015) Modeling consumer behavior (Autumn 2015). J. Consumer Res., https://academic.oup.com/jcr/pages/modeling_consumer_behavior?utm_source=chatgpt.com.Google Scholar
• Kuksov D, Villas-Boas JM (2010) When more alternatives lead to less choice. Marketing Sci. 29(3):507–524.Link, Google Scholar
• Li L, Chen J, Raghunathan S (2018) Recommender system rethink: Implications for an electronic marketplace with competing manufacturers. Inform. Systems Res. 29(4):1003–1023.Link, Google Scholar
• Loyola P, Liu C, Hirate Y (2017) Modeling user session and intent with an attention-based encoder-decoder architecture. Proc. 11th ACM Conf. Recommender Systems (ACM, New York), 147–151.Google Scholar
• MacKenzie I, Meyer C, Noble S (2013) How retailers can keep up with consumers. McKinsey & Company (October 1), https://www.mckinsey.com/industries/retail/our-insights/how-retailers-can-keep-up-with-consumers.Google Scholar
• Moe WW (2003) Buying, searching, or browsing: Differentiating between online shoppers using in-store navigational clickstream. J. Consumer Psych. 13(1–2):29–39.Crossref, Google Scholar
• Moe WW, Fader PS (2004) Capturing evolving visit behavior in clickstream data. J. Interactive Marketing 18(1):5–19.Crossref, Google Scholar
• Montgomery AL, Li S, Srinivasan K, Liechty JC (2004) Modeling online browsing and path analysis using clickstream data. Marketing Sci. 23(4):579–595.Link, Google Scholar
• Rashed A, Elsayed S, Schmidt-Thieme L (2022) Context and attribute-aware sequential recommendation via cross-attention. Proc. 16th ACM Conf. Recommender Systems (ACM, New York), 71–80.Google Scholar
• Shi SW, Zhang J (2014) Usage experience with decision aids and evolution of online purchase behavior. Marketing Sci. 33(6):871–882.Link, Google Scholar
• Song Y, Sun T (2024) Ensemble experiments to optimize interventions along the customer journey: A reinforcement learning approach. Management Sci. 70(8):5115–5130.Link, Google Scholar
• Song Y, Sahoo N, Ofek E (2019) When and how to diversify—A multicategory utility model for personalized content recommendation. Management Sci. 65(8):3737–3757.Link, Google Scholar
• Sun F, Liu J, Wu J, Pei C, Lin X, Ou W, Jiang P (2019) Bert4rec: Sequential recommendation with bidirectional encoder representations from Transformer. Proc. 28th ACM Internat. Conf. Inform. Knowledge Management (CIKM ’19) (Association for Computing Machinery, New York), 1441–1450.Google Scholar
• Ursu R, Seiler S, Honka E (2025) The sequential search model: A framework for empirical research. Quant. Marketing Econom. 23(1):165–213.Crossref, Google Scholar
• Ursu RM, Wang Q, Chintagunta PK (2020) Search duration. Marketing Sci. 39(5):849–871.Link, Google Scholar
• Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser Ł, et al. (2017) Attention is all you need. Guyon I, von Luxburg U, Bengio S, Wallach H, Fergus R, Vishwanathan S, Garnett R, eds. Advances in Neural Information Processing Systems, vol. 30. (Curran Associates Inc., Red Hook, NY).Google Scholar
• Wang D, Yang Q, Abdul A, Lim BY (2019a) Designing theory-driven user-centric explainable AI. Proc. CHI Conf. Human Factors Comput Systems (ACM, New York), 1–15.Google Scholar
• Wang S, Hu L, Wang Y, Cao L, Sheng QZ, Orgun M (2019b) Sequential recommender systems: Challenges, progress and prospects. Preprint, submitted December 28, https://arxiv.org/abs/2001.04830.Google Scholar
• Wei YM, Jiang Z (2025) Estimating parameters of structural models using neural networks. Marketing Sci. 44(1):102–128.Link, Google Scholar
• Weitzman ML (1979) Optimal search for the best alternative. Econometrica 47(3):641–654.Crossref, Google Scholar
• Wu C, Che H, Chan TY, Lu X (2015) The economic value of online reviews. Marketing Sci. 34(5):739–754.Link, Google Scholar
• Zhou C, Bai J, Song J, Liu X, Zhao Z, Chen X, Gao J (2018) Atrank: An attention-based user behavior modeling framework for recommendation. Proc. AAAI Conf. Artificial Intelligence (AAAI Press, Palo Alto, CA), 4564–4571.Google Scholar