HiGR: Industrial-Scale Hierarchical Generative Slate Recommendation Framework in Tencent

Yunsheng Pang; Zijian Liu; Yudong Li; Shaojie Zhu; Zijian Luo; Chenyun Yu; Sikai Wu; Shichen Shen; Cong Xu; Bin Wang; Kai Jiang; Chengxiang Zhuo; Zang Li

arXiv · 2512.24787

HiGR: Industrial-Scale Hierarchical Generative Slate Recommendation Framework in Tencent

Yunsheng Pang·Zijian Liu·Yudong Li·Shaojie Zhu·Zijian Luo·Chenyun Yu·Sikai Wu·Shichen Shen·Cong Xu·Bin Wang·Kai Jiang·Chengxiang Zhuo·Zang Li

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Abstract

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.

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Yunsheng Pang, Zijian Liu, Yudong Li, Shaojie Zhu, Zijian Luo, Chenyun Yu, Sikai Wu, Shichen Shen, Cong Xu, Bin Wang, Kai Jiang, Chengxiang Zhuo, Zang Li. 2025-12-31. HiGR: Industrial-Scale Hierarchical Generative Slate Recommendation Framework in Tencent. https://arxiv.org/abs/2512.24787

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