Probabilistic Digital Twins of Users: Latent Representation Learning with Statistically Validated Semantics
This work addresses the need for interpretable and uncertainty-aware user representations in applications like personalization and recommendation, though it is incremental as it builds on existing variational autoencoder methods.
The paper tackled the problem of understanding user identity and behavior by proposing a probabilistic digital twin framework that models users as latent stochastic states, resulting in interpretable, uncertainty-aware representations with weak but meaningful structure along dominant latent axes.
Understanding user identity and behavior is central to applications such as personalization, recommendation, and decision support. Most existing approaches rely on deterministic embeddings or black-box predictive models, offering limited uncertainty quantification and little insight into what latent representations encode. We propose a probabilistic digital twin framework in which each user is modeled as a latent stochastic state that generates observed behavioral data. The digital twin is learned via amortized variational inference, enabling scalable posterior estimation while retaining a fully probabilistic interpretation. We instantiate this framework using a variational autoencoder (VAE) applied to a user-response dataset designed to capture stable aspects of user identity. Beyond standard reconstruction-based evaluation, we introduce a statistically grounded interpretation pipeline that links latent dimensions to observable behavioral patterns. By analyzing users at the extremes of each latent dimension and validating differences using nonparametric hypothesis tests and effect sizes, we demonstrate that specific dimensions correspond to interpretable traits such as opinion strength and decisiveness. Empirically, we find that user structure is predominantly continuous rather than discretely clustered, with weak but meaningful structure emerging along a small number of dominant latent axes. These results suggest that probabilistic digital twins can provide interpretable, uncertainty-aware representations that go beyond deterministic user embeddings.