Slate recommendation, which presents users with a ranked item list in a single display, is ubiquitous across mainstream online platforms. While recent generative recommendation methods have shown strong potential in modeling item sequences with semantic IDs, directly applying them to industrial-scale slate recommendation faces a fundamental disconnect: entangled SID spaces confound high-level list planning, fine-grained autoregressive decoding over long sequences limits semantic planning efficiency, and token-level objectives misalign with holistic slate quality. In this paper, we propose HiGR, an industrial-scale hierarchical generative framework for slate recommendation that bridges this disconnect through a co-designed pipeline. First, HiGR learns structured SIDs via a Prefix-Contrastive Residual Quantized VAE (PCRQ-VAE). By enforcing high-level prefixes to capture shared semantics, PCRQ-VAE creates a controllable discrete space that acts as a prerequisite for efficient planning. Leveraging this structured space, our Hierarchical Slate Decoder (HSD) shifts autoregressive modeling from entangled token-level decoding to coarse-grained preference embeddings. This design significantly reduces inference latency while allowing explicit global slate structure planning. Finally, this stable planning space enables an ORPO-based listwise alignment mechanism to optimize triple-objective implicit feedback-ranking fidelity, genuine user interest, and diversity. Extensive offline experiments show that HiGR outperforms state-of-the-art baselines by over 10% in offline recommendation quality while achieving a $5\times$ inference speedup. Online A/B tests on Tencent platforms further improve watch time by 1.22% and video plays by 1.73%. HiGR has been deployed on multiple Tencent platform surfaces, serving hundreds of millions of users and proving its industrial-scale applicability.

翻译：列表推荐（slate recommendation）以单一展示页面向用户呈现排序后的条目列表，是主流在线平台的常见功能。尽管近期生成式推荐方法在利用语义ID建模项目序列方面展现出强大潜力，但将其直接应用于工业级列表推荐存在根本性脱节：纠缠的SID空间混淆了高层级列表规划，长序列上细粒度自回归解码限制了语义规划效率，而令牌级目标与整体列表质量不匹配。本文提出HiGR——一种专为列表推荐设计的工业级层次化生成框架，通过协同设计的流水线弥合上述断层。首先，HiGR通过前缀对比残差量化VAE（PCRQ-VAE）学习结构化SID。通过强制高层级前缀捕获共享语义，PCRQ-VAE创建了可供调控的离散空间，为高效规划奠定基础。利用该结构化空间，我们的层次化列表解码器（HSD）将自回归建模从纠缠的令牌级解码转变为粗粒度偏好嵌入。此设计显著降低推理延迟，同时允许显式的全局列表结构规划。最后，这一稳定的规划空间使得基于ORPO的列表级对齐机制能够优化三重隐式反馈目标——排序保真度、真实用户兴趣与多样性。大量离线实验表明，HiGR在离线推荐质量上较最优基线提升超10%，同时实现5倍推理加速。腾讯平台的在线A/B测试进一步将观看时长提升1.22%，视频播放量提升1.73%。目前HiGR已部署于多个腾讯平台界面，服务数亿用户，验证了其工业级适用性。