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

Given the increasing importance of user engagement on digital platforms, this paper proposes a Bayesian deep-learning model called the Multi-Dynamic Neural Poisson System (MDNPS), which can capture the multifaceted nature of user sparse but high-dimensional activities on such platforms. MDNPS yields semantically interpretable factors underlying high-dimensional items, quantifies user preferences at different granularity levels, and adeptly captures their temporal and cross-activity dependencies. This model is scalable to large empirical data and can be inferred efficiently with a stochastic variational Bayes algorithm. We apply MDNPS to the largest knowledge-sharing platform in China, focusing on the dynamics in user content consumption and contribution behaviors. We show that MDNPS significantly outperforms benchmark factor models in factor quality and out-of-sample data fitting. Our platform-level and individual-level estimates show rich and interpretable insights about consumer preference dynamics and their relationships with user popularity. These insights offer valuable managerial implications for platforms to understand user segments, personalize content offerings, and enhance user engagement.

History: Tat Chan served as the senior editor for this article.

Funding: This work was supported by the University Grants Committee of the Hong Kong Special Administrative Region (Project No. 16500124, 2024-27). All authors certify that they have no affiliations with or involvement in any organization or entity with any financial interest or nonfinancial interest in the subject matter or materials discussed in this manuscript. M. Yin acknowledges the support from the Marketing Science Institute and the Warrington Commitment Research Award. The authors acknowledge the computational support from UF Research Computing and HiPerGator.

Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mksc.2024.1090.