发表机构
Spotify（声田公司）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究多目标生成推荐系统，提出轻量级推理时解码层，通过随机原始对偶近似方案平衡相关性与辅助目标，经实验验证可在多目标权衡上持续改进，提升辅助目标且不影响用户满意度。
AI 中文摘要
推荐系统（RS）的最新进展通过生成建模实现了显著的性能提升。在实际应用中，推荐通常涉及构建列表——有序的项目列表——其必须满足相关性之外的多个目标，例如基于项目属性定义的约束或公平性约束。现有的多目标方法要么依赖于为非生成设置设计的后处理技术，要么将辅助目标直接纳入模型训练。前者没有明确考虑生成式RS的顺序性质，而后者在大规模系统中通常不切实际。我们提出了一个轻量级的推理时解码层，它增强了自回归生成式RS以支持多目标列表生成，而无需修改或重新训练基础模型。我们将解码公式化为一个在线约束优化问题，其中项目是顺序选择的，并且基于剩余的约束松弛动态调整相关性和辅助目标之间的权衡。这是通过一种随机原始对偶近似方案实现的，该方案在生成过程中平衡相关性和辅助目标。我们提供了关于约束违反和遗憾的理论保证，并通过广泛的离线实验和在实际推荐系统中的大规模在线A/B实验评估了所提出的方法。我们的结果表明，在多目标权衡方面有持续的改进，包括在不影响用户满意度的情况下，辅助目标实现了1.8%的增益。
英文摘要
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.
