HiGR: Hierarchical Generative Framework for Industrial-Scale Slate Recommendation
Tencent researchers have published HiGR, a hierarchical generative framework designed to improve slate recommendation — the ranked lists of items shown to users on online platforms — at industrial scale. The system addresses key limitations of existing generative recommendation methods, including slow inference and poor alignment between token-level training objectives and overall list quality. Deployed across multiple Tencent platforms serving hundreds of millions of users, HiGR demonstrated measurable gains in watch time and video plays in live A/B testing.
HiGR (Hierarchical Generative Recommendation) is a co-designed pipeline introduced by Tencent researchers to overcome fundamental challenges in applying generative AI methods to real-world slate recommendation systems. The framework introduces a Prefix-Contrastive Residual Quantized VAE (PCRQ-VAE) to learn structured semantic IDs, where high-level prefixes capture shared item semantics and create a controllable discrete space suited for planning. Building on this, a Hierarchical Slate Decoder (HSD) replaces fine-grained token-level autoregressive decoding with coarser preference-embedding-level decoding, reducing inference latency by approximately 5× compared to prior approaches. An ORPO-based listwise alignment mechanism then jointly optimizes for ranking fidelity, genuine user interest, and diversity — three objectives that token-level training typically conflates or ignores. In offline evaluations, HiGR outperformed state-of-the-art baselines by more than 10% on recommendation quality metrics. Online A/B tests on Tencent platforms showed a 1.22% increase in watch time and a 1.73% increase in video plays, with the system now deployed at scale serving hundreds of millions of users.
What's missing
The paper does not report statistical significance or confidence intervals for the A/B test results. The study's offline evaluation benchmarks and datasets are not described in the abstract, making independent reproducibility assessment difficult. Long-term effects on user behavior, potential filter-bubble or engagement-optimization concerns, and fairness implications for content creators are not addressed.
What different sources said
- arXiv cs.AICenter
HiGR: Industrial-Scale Hierarchical Generative Slate Recommendation Framework in Tencent
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