HiGR: Hierarchical Generative Framework for Industrial-Scale Slate Recommendation
Researchers at Tencent developed HiGR, a new recommendation system that generates ranked lists of items for users more efficiently than previous methods. The framework uses a hierarchical approach with structured semantic IDs and a specialized decoder to balance planning efficiency with recommendation quality. The system has been deployed across Tencent platforms serving hundreds of millions of users, with online tests showing improvements in user engagement metrics.
HiGR addresses fundamental challenges in applying generative recommendation methods to industrial-scale slate recommendation—the task of presenting users with ranked item lists. The framework consists of three main components: a Prefix-Contrastive Residual Quantized VAE (PCRQ-VAE) that learns structured semantic IDs, a Hierarchical Slate Decoder (HSD) that shifts from token-level to preference-level autoregressive modeling, and an ORPO-based alignment mechanism optimizing for ranking fidelity, user interest, and diversity. Offline experiments demonstrated over 10% improvement in recommendation quality compared to state-of-the-art baselines alongside a 5× inference speedup. Online A/B testing on Tencent platforms showed measurable improvements: 1.22% increase in watch time and 1.73% increase in video plays. The system is currently deployed across multiple Tencent platform surfaces.
What's missing
The paper does not discuss potential limitations of the approach, such as computational requirements for training, generalization to platforms outside Tencent's ecosystem, or how the system handles cold-start problems for new users or items. Additionally, no discussion of fairness, bias, or potential negative societal impacts of the recommendation system is provided.
What different sources said
- arXiv cs.AICenter
HiGR: Industrial-Scale Hierarchical Generative Slate Recommendation Framework in Tencent
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