Paper Number
ICIS2025-1536
Paper Type
Complete
Abstract
The increasing availability of digital trace data offers opportunities for representational richness in modeling user behavioral patterns with downstream implications in socio- technical contexts, but poses challenges due to its multidimensionality, longitudinality, and sparsity. We propose a Bayesian tensor-based framework, including a novel empir- ical Bayesian tensor decomposition method, that represents the interplay between users, platform/sensor-based channels, and time using a three-dimensional tensor. Collectively, our framework and method capture user behaviors via low-dimensional latent factors using a family of sparsity-induced priors, thereby enhancing explanatory and predic- tive power. Simulations show our algorithm outperforms alternatives with less bias and stronger predictive power. We further demonstrate practical value through two large- scale case studies—online customer journeys from omnichannel clickstreams and patient behavior in telehealth—and an expert study with healthcare professionals. Together, these findings highlight the framework’s ability to produce relevant, interpretable, and action- able insights, underscoring its broader implications for computational design in socio- technical environments.
Recommended Citation
Zhang, Xinyuan; Cai, Jeff; Li, Jingjing; and Abbasi, Ahmed, "Empirical Bayes Tensor Decomposition: A Holistic and Interpretable Representation of Digital Trace Patterns" (2025). ICIS 2025 Proceedings. 7.
https://aisel.aisnet.org/icis2025/is_researchmethods/is_researchmethods/7
Empirical Bayes Tensor Decomposition: A Holistic and Interpretable Representation of Digital Trace Patterns
The increasing availability of digital trace data offers opportunities for representational richness in modeling user behavioral patterns with downstream implications in socio- technical contexts, but poses challenges due to its multidimensionality, longitudinality, and sparsity. We propose a Bayesian tensor-based framework, including a novel empir- ical Bayesian tensor decomposition method, that represents the interplay between users, platform/sensor-based channels, and time using a three-dimensional tensor. Collectively, our framework and method capture user behaviors via low-dimensional latent factors using a family of sparsity-induced priors, thereby enhancing explanatory and predic- tive power. Simulations show our algorithm outperforms alternatives with less bias and stronger predictive power. We further demonstrate practical value through two large- scale case studies—online customer journeys from omnichannel clickstreams and patient behavior in telehealth—and an expert study with healthcare professionals. Together, these findings highlight the framework’s ability to produce relevant, interpretable, and action- able insights, underscoring its broader implications for computational design in socio- technical environments.
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