Stochastic Primal-Dual Decoding for Multiobjective Generative Recommender Systems
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.
- ▪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.
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Computer Science > Artificial Intelligence arXiv:2607.19357 (cs) [Submitted on 26 May 2026] Title:Stochastic Primal-Dual Decoding for Multiobjective Generative Recommender Systems Authors:Dmitrii Moor, Ben Carterette, Senthilkumar Krishnamoorthy, Kyle Kretschman, Denis Beslic, Melissa Yalla, Alice Y Wang, Mounia Lalmas View a PDF of the paper titled Stochastic Primal-Dual Decoding for Multiobjective Generative Recommender Systems, by Dmitrii Moor and 7 other authors View PDF HTML (experimental) Abstract:Recent advances in recommender systems (RS) have shown substantial performance gains through generative modelling.
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