让小模型更懂推荐，提升速度与准确率
SmartGR: Hierarchy and Beam-Aware Knowledge Distillation for Generative Recommendation
- 分层传递知识，解决推荐中不同层级难度不均问题
- 优化生成过程中的候选筛选，提升推荐准确率8.6%
- 适合需要快速生成推荐结果的场景，如实时推荐
生成式推荐（GR）是推荐系统的新范式。扩大模型规模可提升性能，但推理成本也大幅增加。知识蒸馏通过将大模型的知识迁移到小模型来缓解此问题。然而现有方法未考虑两个GR特有挑战：语义ID（SID）层次结构中蒸馏难度不均衡，以及束搜索中错误的前缀剪枝。为此，我们提出SmartGR，一种新蒸馏框架，采用分层感知的SID蒸馏，实现跨层次建模能力迁移；并通过束搜索感知的排序蒸馏，捕捉教师模型在束搜索中的偏好。在四个基准数据集上的大量实验表明，SmartGR平均提升性能8.6%，推理速度提高2.39倍。
原文摘要 · Abstract (English)
Generative recommendation (GR) has emerged as a promising paradigm for recommender systems. Scaling up GR models can improve recommendation performance, but it also substantially increases inference cost. Knowledge distillation provides a practical solution by transferring knowledge from a large GR model to a lightweight one. However, existing distillation methods do not account for two GR-specific challenges: imbalanced distillation difficulty across the semantic ID (SID) hierarchy and incorrect prefix pruning during beam search. To address these challenges, we propose SmartGR, a novel distillation framework that utilizes Hierarchy-Aware SID Distillation to transfer the teacher's modeling capability across the hierarchy and leverages Beam-Aware Ranking Distillation to distill the teacher's ranking preferences during beam search. Extensive experiments on four benchmark datasets demonstrate the effectiveness and efficiency of SmartGR, improving the performance by 8.6% while achieving a 2.39$\times$ inference speedup on average.
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