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Tencent unveils HiGR for industrial-scale generative slate recommendation

By PulseAugur Editorial · [1 sources] ·

Tencent has developed HiGR, a hierarchical generative framework for industrial-scale slate recommendation. This system addresses challenges in applying generative models to large-scale recommendation by learning structured item IDs and shifting autoregressive modeling to preference embeddings for efficient planning. HiGR has demonstrated significant improvements in offline recommendation quality and inference speed, and has been successfully deployed on Tencent platforms, enhancing user engagement metrics. AI

IMPACT This framework could significantly improve recommendation efficiency and effectiveness for platforms serving hundreds of millions of users.

RANK_REASON Academic paper published on arXiv detailing a new framework for slate recommendation. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Tencent unveils HiGR for industrial-scale generative slate recommendation

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Academic paper published on arXiv detailing a new framework for slate recommendation. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · 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 ·

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

    arXiv:2512.24787v3 Announce Type: replace-cross 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 modeli…

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