Stochastic Primal-Dual Decoding for Multiobjective Generative Recommender Systems
This paper introduces a lightweight inference-time decoding layer that enhances autoregressive generative recommender systems to support multiobjective slate generation without retraining. It formulates decoding as an online constrained optimization problem, dynamically adjusting trade-offs between relevance and auxiliary objectives via a stochastic primal-dual approximation scheme. Theoretical guarantees on constraint violation and regret are provided. Extensive offline experiments and a large-scale online A/B test demonstrate consistent improvements in multiobjective trade-offs, including a +1.8% gain in the auxiliary objectives achieved at zero cost to user satisfaction.
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[Submitted on 26 May 2026]
Title:Stochastic Primal-Dual Decoding for Multiobjective Generative Recommender Systems
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Abstract:Recent advances in recommender systems (RS) have shown substantial performance gains through generative modelling. In practice, recommendation often involves constructing slates -- ordered lists of items -- that must satisfy multiple objectives beyond relevance, such as constraints defined over item attributes or fairness constraints. Existing multiobjective approaches either rely on post-processing techniques designed for non-generative settings, or incorporate auxiliary objectives directly into model training. The former does not explicitly account for the sequential nature of generative RS, while the latter is often impractical in large-scale systems.
We propose a lightweight, inference-time decoding layer that augments autoregressive generative RS to support multiobjective slate generation without modifying or retraining the underlying model. We formulate decoding as an online constrained optimisation problem, where items are selected sequentially, and trade-offs between relevance and auxiliary objectives are adjusted dynamically based on the remaining constraint slack, i.e., how much of each objective remains to be satisfied. This is implemented via a stochastic primal-dual approximation scheme that balances relevance and auxiliary objectives during generation.
We provide theoretical guarantees on constraint violation and regret, and evaluate the proposed approach through extensive offline experiments and a large-scale online A/B experiment in a real-world recommender system. Our results show consistent improvements in multiobjective trade-offs, including a +1.8\% gain in the auxiliary objectives achieved at zero cost to user satisfaction.
Subjects:
Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
Cite as: arXiv:2607.19357 [cs.AI]
(or arXiv:2607.19357v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2607.19357
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Dmitrii Moor [view email] [v1] Tue, 26 May 2026 15:05:06 UTC (1,130 KB)
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