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

Mingzhang Yin et al.

Marketing Science2026https://doi.org/10.1287/mksc.2024.1090article
FT50UTD24AJG 4*ABDC A*
Weight
0.50

What the paper says

This paper proposes a Bayesian deep-learning model that captures multifaceted, time-varying user activities on digital platforms, yielding interpretable and dynamic preference estimations at the platform and individual levels.

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Cite this paper

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

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@article{mingzhang2026,
  title        = {{Unraveling Multifaceted User Preferences on Digital Platforms: A Bayesian Deep-Learning Approach}},
  author       = {Mingzhang Yin et al.},
  journal      = {Marketing Science},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1287/mksc.2024.1090},
}

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

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Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.