Slate recommendation, which presents users with a ranked item list in a single display, is ubiquitous across mainstream online platforms. Recent advances in generative models have shown significant potential for this task via autoregressive modeling of discrete semantic ID sequences. However, existing methods suffer from three key limitations: entangled item tokenization, inefficient sequential decoding, and the absence of holistic slate planning. These issues often result in substantial inference overhead and inadequate alignment with diverse user preferences and practical business requirements, hindering the industrial deployment of generative slate recommendation systems. In this paper, we propose HiGR, an efficient generative slate recommendation framework that integrates hierarchical planning with listwise preference alignment. First, we design an auto-encoder incorporating residual quantization and contrastive constraints, which tokenizes items into semantically structured IDs to enable controllable generation. Second, HiGR decouples the generation process into two stages: a list-level planning stage to capture global slate intent, and an item-level decoding stage to select specific items, effectively reducing the search space and enabling efficient generation. Third, we introduce a multi-objective and listwise preference alignment mechanism that enhances slate quality by leveraging implicit user feedback. Extensive experiments have validated the effectiveness of our HiGR method. Notably, it outperforms state-of-the-art baselines by over 10\% in offline recommendation quality while achieving a $5\times$ inference speedup. Furthermore, we have deployed HiGR on a commercial platform under Tencent (serving hundreds of millions of users), and online A/B tests show that it increases average watch time and average video plays by 1.22\% and 1.73\%, respectively.

翻译：列表推荐在主流在线平台中无处不在，它通过单次展示向用户呈现一个排序后的项目列表。生成模型的最新进展通过离散语义ID序列的自回归建模，在该任务中展现出巨大潜力。然而，现有方法存在三个关键局限：纠缠的项目标记化、低效的顺序解码以及缺乏整体列表规划。这些问题通常导致显著的推理开销，且难以与多样化的用户偏好及实际业务需求充分对齐，阻碍了生成式列表推荐系统的工业部署。本文提出HiGR，一个高效生成式列表推荐框架，它集成了分层规划与列表级偏好对齐。首先，我们设计了一个结合残差量化与对比约束的自编码器，将项目标记化为语义结构化的ID，以实现可控生成。其次，HiGR将生成过程解耦为两个阶段：列表级规划阶段以捕捉全局列表意图，以及项目级解码阶段以选择具体项目，这有效缩小了搜索空间并实现了高效生成。第三，我们引入了一种多目标且列表级的偏好对齐机制，通过利用隐式用户反馈来提升列表质量。大量实验验证了我们HiGR方法的有效性。值得注意的是，它在离线推荐质量上以超过10%的优势超越了现有最先进的基线方法，同时实现了$5\times$的推理加速。此外，我们已在腾讯旗下的一个商业平台（服务数亿用户）上部署了HiGR，在线A/B测试表明，它分别将平均观看时长和平均视频播放量提升了1.22%和1.73%。