Unraveling Multifaceted User Preferences on Digital Platforms: A Bayesian Deep-Learning Approach

Published Online:https://doi.org/10.1287/mksc.2024.1090

References

  • Atchison J, Shen SM (1980) Logistic-normal distributions: Some properties and uses. Biometrika 67(2):261–272.CrossrefGoogle Scholar
  • Blei DM, Lafferty JD (2006) Dynamic topic models. Cohen W, Moore A, eds. Proc. 23rd Internat. Conf. Machine Learn. (Association for Computing Machinery, New York), 113–120.Google Scholar
  • Blei DM, Kucukelbir A, McAuliffe JD (2017) Variational inference: A review for statisticians. J. Amer. Stat. Assoc. 112(518):859–877.CrossrefGoogle Scholar
  • Blei DM, Ng AY, Jordan MI (2003) Latent dirichlet allocation. J. Machine Learn. Res. 3(Jan):993–1022.Google Scholar
  • Cain P (2022) Modelling short-and long-term marketing effects in the consumer purchase journey. Internat. J. Res. Marketing 39(1):96–116.CrossrefGoogle Scholar
  • Chakraborty I, Kim M, Sudhir K (2022) Attribute sentiment scoring with online text reviews: Accounting for language structure and missing attributes. J. Marketing Res. 59(3):600–622.CrossrefGoogle Scholar
  • Cheng Z, Lee D, Tambe P (2022) InnoVAE: Generative AI for understanding patents and innovation. Preprint, submitted April 13, http://dx.doi.org/10.2139/ssrn.3868599.Google Scholar
  • DeHaan E, Kim J, Lourie B, Zhu C (2024) Buy now pay (pain?) later. Management Sci. 70(8):5586–5598.LinkGoogle Scholar
  • Dew R, Ansari A, Li Y (2020) Modeling dynamic heterogeneity using gaussian processes. J. Marketing Res. 57(1):55–77.CrossrefGoogle Scholar
  • Dew R, Padilla N, Luo LE, Oblander S, Ansari A, Boughanmi K, Braun M, et al. (2024) Probabilistic machine learning: New frontiers for modeling consumers and their choices. Preprint, submitted April 16, http://dx.doi.org/10.2139/ssrn.4790799.Google Scholar
  • Dhillon PS, Aral S (2021) Modeling dynamic user interests: A neural matrix factorization approach. Marketing Sci. 40(6):1059–1080.AbstractGoogle Scholar
  • Dieng AB, Ruiz FJ, Blei DM (2019) The dynamic embedded topic model. Preprint, submitted July 12, https://arxiv.org/abs/1907.05545.Google Scholar
  • Dieng AB, Ruiz FJ, Blei DM (2020) Topic modeling in embedding spaces. Trans. Assoc. Comput. Linguist. 8:439–453.CrossrefGoogle Scholar
  • Duvvuri SD, Ansari A, Gupta S (2007) Consumers’ price sensitivities across complementary categories. Management Sci. 53(12):1933–1945.LinkGoogle Scholar
  • Fox V (2007) Pew/internet study finds most americans get their answers from the internet. https://searchengineland.com/pewinternet-study-finds-most-americans-get-their-answers-from-the-internet-13028.Google Scholar
  • Gabel S, Timoshenko A (2022) Product choice with large assortments: A scalable deep-learning model. Management Sci. 68(3):1808–1827.LinkGoogle Scholar
  • Goli A, Chintagunta PK, Sriram S (2022) Effects of payment on user engagement in online courses. J. Marketing Res. 59(1):11–34.CrossrefGoogle Scholar
  • Gong C, Huang W-B (2017) Deep dynamic poisson factorization model. Advances in Neural Information Processing Systems 30.Google Scholar
  • Goodfellow I, Bengio Y, Courville A (2016) Deep Learning (MIT Press, Cambridge, MA).Google Scholar
  • Gopalan PK, Charlin L, Blei D (2014) Content-based recommendations with poisson factorization. Ghahramani Z, Welling M, Cortes C, Lawrence ND, Weinberger KQ, eds. Advances in Neural Information Processing Systems, vol. 27 (Neural Information Processing Systems Foundation, Red Hook, NY).Google Scholar
  • Guadagni PM, Little JD (1983) A logit model of brand choice calibrated on scanner data. Marketing Sci. 2(3):203–238.LinkGoogle Scholar
  • Guo D, Chen B, Zhang H, Zhou M (2018) Deep poisson gamma dynamical systems. Bengio S, Wallach HM, Larochelle H, Grauman K, Cesa-Bianchi N, Garnett R, eds. Advances in Neural Information Processing Systems, vol. 31 (Neural Information Processing Systems Foundation, Red Hook, NY).Google Scholar
  • Hemann C, Burbary K (2013) Digital Marketing Analytics: Making Sense of Consumer Data in a Digital World (Pearson Education, Indianapolis).Google Scholar
  • Hochreiter S, Schmidhuber J (1997) Long short-term memory. Neural Comput. 9(8):1735–1780.CrossrefGoogle Scholar
  • Hoffman MD, Blei DM, Wang C, Paisley J (2013) Stochastic variational inference. J. Machine Learn. Res. 14(1):1303–1347.Google Scholar
  • Horrigan JB (2001) Online communities. PEW RESEARCH CENTER.Google Scholar
  • Jacobs B, Fok D, Donkers B (2021) Understanding large-scale dynamic purchase behavior. Marketing Sci. 40(5):844–870.LinkGoogle Scholar
  • Kingma DP, Ba J (2014) Adam: A method for stochastic optimization. Preprint, submitted December 22, https://arxiv.org/abs/1412.6980.Google Scholar
  • Kingma DP, Welling M (2013) Auto-encoding variational bayes. ICLR. https://arxiv.org/abs/1312.6114.Google Scholar
  • Kipf TN, Welling M (2016) Semi-supervised classification with graph convolutional networks. Preprint, submitted September 9, https://arxiv.org/abs/1609.02907.Google Scholar
  • Koksal I (2020). The rise of online learning. Accessed July 27, 2024, https://www.forbes.com/sites/ilkerkoksal/2020/05/02/the-rise-of-online-learning/?sh=6c9a2daa72f3.Google Scholar
  • Li H, Ma L (2020) Charting the path to purchase using topic models. J. Marketing Res. 57(6):1019–1036.CrossrefGoogle Scholar
  • Li Y, Wang C, Duan Z, Wang D, Chen B, An B, Zhou M (2022) Alleviating “posterior collapse” in deep topic models via policy gradient. Adv. Neural Inform. Processing Systems, vol. 35 (Curran Associates, Inc., Red Hook, NY), 22562–22575.Google Scholar
  • Liberali G, Ferecatu A (2022) Morphing for consumer dynamics: Bandits meet hidden markov models. Marketing Sci. 41(4):769–794.LinkGoogle Scholar
  • Liu X (2023) Deep learning in marketing: A review and research agenda. Artificial Intelligence Marketing 20:239–271.Google Scholar
  • Liu J, Cong Z (2023) The daily me versus the daily others: How do recommendation algorithms change user interests? Evidence from a knowledge-sharing platform. J. Marketing Res. 60(4):767–791.CrossrefGoogle Scholar
  • Liu L, Dzyabura D (2021) Capturing heterogeneity among consumers with multitaste preferences. Working paper, New York University Stern School of Business, New York.Google Scholar
  • Liu J, Kawaguchi K, Li T (2024) Segmenting consumer location-product preferences for assortment localization. Working paper, Hong Kong University of Science & Technology (HKUST), Hong Kong.Google Scholar
  • Liu X, Lee D, Srinivasan K (2019) Large-scale cross-category analysis of consumer review content on sales conversion leveraging deep learning. J. Marketing Res. 56(6):918–943.CrossrefGoogle Scholar
  • Liu J, Toubia O, Hill S (2021) Content-based model of web search behavior: An application to TV show search. Management Sci. 67(10):6378–6398.LinkGoogle Scholar
  • Lu Z, Kannan P (2026) AI for customer journeys: A transformer approach. J. Marketing Res. 63(1):1–26.CrossrefGoogle Scholar
  • Malhotra NK, Sudhir K, Toubia O (2023) Artificial Intelligence in Marketing, Review of Marketing Research, vol. 20 (Emerald Publishing Limited, Bingley, UK).Google Scholar
  • McInnes L, Healy J, Saul N, Groβberger L (2018) UMAP: Uniform manifold approximation and projection. J. Open Source Software 3(29):861.CrossrefGoogle Scholar
  • Nie L, Li Y, Feng F, Song X, Wang M, Wang Y (2020) Large-scale question tagging via joint question-topic embedding learning. ACM Trans. Inform. Systems (TOIS) 38(2):1–23.CrossrefGoogle Scholar
  • Ouyang S (2021). Zhihu bets big on China’s knowledge-sharing market. Accessed July 29, 2024, https://global.chinadaily.com.cn/a/202101/14/WS5fffde7ca31024ad0baa29fb.html.Google Scholar
  • Puranam D, Kadiyali V, Narayan V (2021) The impact of increase in minimum wages on consumer perceptions of service: A transformer model of online restaurant reviews. Marketing Sci. 40(5):985–1004.LinkGoogle Scholar
  • Reimers N, Gurevych I (2019) Sentence-bert: Sentence embeddings using siamese bert-networks. Inui K, Jiang J, Ng V, Wan X, eds. Proc. EMNLP-IJCNLP (Association for Computational Linguistics, Stroudsburg, PA), 3982–3992.Google Scholar
  • Röder M, Both A, Hinneburg A (2015) Exploring the space of topic coherence measures. Bendersky M, Najork M, Singla A, eds. Proc. Eighth ACM Internat. Conf. Web Search Data Mining (Association for Computing Machinery, New York), 399–408.Google Scholar
  • Ruiz FJR, Athey S, Blei DM (2020) SHOPPER: A probabilistic model of consumer choice with substitutes and complements. Ann. Appl. Stat. 14(1):1–27.CrossrefGoogle Scholar
  • Schein A, Wallach H, Zhou M (2016) Poisson-gamma dynamical systems. Lee DD, Sugiyama M, von Luxburg U, Guyon I, Garnett R, eds. Advances in Neural Information Processing Systems, vol. 29 (Neural Information Processing Systems Foundation, Red Hook, NY).Google Scholar
  • Schein A, Paisley J, Blei DM, Wallach H (2015) Bayesian Poisson tensor factorization for inferring multilateral relations from sparse dyadic event counts. Cao L, Zhang C, Li T, Lin T, eds. Proc. 21th ACM SIGKDD Internat. Conf. Knowledge Discovery Data Mining (Association for Computing Machinery, New York), 1045–1054.Google Scholar
  • Schein A, Linderman S, Zhou M, Blei D, Wallach H (2019) Poisson-randomized gamma dynamical systems. Adv. Neural Inform. Processing Systems, vol. 32 (Curran Associates Inc., Red Hook, NY).Google Scholar
  • Sisodia A, Burnap A, Kumar V (2025) Generative interpretable visual design: Using disentanglement for visual conjoint analysis. J. Marketing Res. 62(3):405–428.Google Scholar
  • Tian Z, Dew R, Iyengar R (2024) Mega or micro? Influencer selection using follower elasticity. J. Marketing Res. 61(3):472–495.CrossrefGoogle Scholar
  • Toubia O (2021) A poisson factorization topic model for the study of creative documents (and their summaries). J. Marketing Res. 58(6):1142–1158.CrossrefGoogle Scholar
  • Wainwright MJ, Jordan MI (2008) Graphical models, exponential families, and variational inference. Foundations and Trends® in Mach. Learn. 1(1–2):1–305.CrossrefGoogle Scholar
  • Wallach HM, Murray I, Salakhutdinov R, Mimno D (2009) Evaluation methods for topic models. Proc. 26th Annual Internat. Conf. Machine Learn., 1105–1112.Google Scholar
  • Wang H, Yeung DY (2020) A survey on Bayesian deep learning. ACM Comput. Surveys 53(5):1–37.Google Scholar
  • West R, Leskovec J, Potts C (2021) Postmortem memory of public figures in news and social media. Proc. Natl. Acad. Sci. USA 118(38):e2106152118.CrossrefGoogle Scholar
  • Wu X, Dong X, Nguyen TT, Luu AT (2023) Effective neural topic modeling with embedding clustering regularization. Internat. Conf. Machine Learn. (PMLR, New York), 37335–37357.Google Scholar
  • Yang S, Allenby GM (2003) Modeling interdependent consumer preferences. J. Marketing Res. 40(3):282–294.CrossrefGoogle Scholar
  • Yao E, Zheng G, Jin O, Bao S, Chen K, Su Z, Yu Y (2014) Probabilistic text modeling with orthogonalized topics. Geva S, Trotman A, Bruza P, Clarke CLA, Järvelin K, eds. Proc. 37th Internat. ACM SIGIR Conf. Res. Development Inform. Retrieval (Association for Computing Machinery, New York), 907–910.Google Scholar
  • Yin M, Zhou M (2018) Semi-implicit variational inference. Internat. Conf. Machine Learn. (PMLR, New York), 5660–5669.Google Scholar
  • Yin J, Feng Y, Liu Y (2024a) Modeling behavioral dynamics in digital content consumption: An attention-based neural point process approach with applications in video games. Marketing Sci. 44(1):220–239.Google Scholar
  • Yin M, Gao R, Cong Z (2025) Personalizing language models for generative targeting. Preprint, submitted June 30, http://dx.doi.org/10.2139/ssrn.5329729.Google Scholar
  • Yin M, Boughanmi K, Mukherjee A, Ansari A (2024b) Meta-learning customer preference dynamics for fast customization on digital platforms. Preprint, submitted March 13, https://ssrn.com/abstract=4727171.Google Scholar
  • Yu Y, Si X, Hu C, Zhang J (2019) A review of recurrent neural networks: Lstm cells and network architectures. Neural Comput. 31(7):1235–1270.CrossrefGoogle Scholar
  • Zhou M, Chen H, Ren L, Sapiro G, Carin L, Paisley J (2009) Non-parametric bayesian dictionary learning for sparse image representations. Adv. Neural Inform. Processing Systems, vol. 22 (Curran Associates Inc., Red Hook, NY).Google Scholar
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