Probabilistic Residual Learning for Online Recommendations

• URL: http://arxiv.org/abs/2607.20863v1
• Date: Thu, 23 Jul 2026 02:39:12 GMT
• Title: Probabilistic Residual Learning for Online Recommendations
• Abstract summary: Probabilistic Residual Learning (PRL) is a causal Bayesian recommendation model that groups users for localized residual modeling.<n>PRL is compatible with various base deep learning recommender systems, improving their performance while automatically discovering meaningful user clusters.
• Score: 50.32543279885484
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Modern recommender systems are typically based on deep learning (DL) models, where a dense encoder learns representations of users and items. As a result, these systems often suffer from the black-box nature and computational complexity of the underlying models, making it difficult to systematically enhance their recommendation capabilities. To address this problem, we propose Probabilistic Residual Learning (PRL), a causal Bayesian recommendation model that models the residual between ground-truth and base predictions, enabling targeted refinement of existing systems. Specifically, PRL (1) probabilistically groups users for localized residual modeling, (2) models domain-level confounders that influence user and item representations, and (3) aggregates cluster-specific residual predictions over the confounders using do-calculus. Experiments demonstrate that our plug-and-play PRL is compatible with various base deep learning recommender systems, improving their performance while automatically discovering meaningful user clusters.

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