针对推荐系统知识蒸馏中交叉熵失效问题，提出新方法提升排名精度。
Rejuvenating Cross-Entropy Loss in Knowledge Distillation for Recommender Systems
- 将教师模型的顶部项目按学生模型排名分组，分治处理
- 在小样本子集上使NDCG下界最大化，实测性能提升12.3%以上
- 适合需要高精度排序蒸馏的推荐系统场景
本文分析了推荐系统知识蒸馏中交叉熵（CE）损失的局限性。由于知识蒸馏仅在少量候选项目上计算，且目标是蒸馏教师模型最可能被偏好的项目排名，而传统方法假设学生模型的顶部项目与教师一致，这一假设与实际目标矛盾。实验显示两者顶部项目差异显著。为此，提出重生成交叉熵知识蒸馏（RCE-KD）：将教师顶部项目按是否被学生高排名分为两组，对不满足闭包假设的组采用教师-学生协作采样逼近该假设，并自适应融合两组损失。大量实验表明，该方法在Recall@10、NDCG@10等指标上显著优于基线，性能提升达12.3%以上。代码已公开于https://github.com/BDML-lab/RCE-KD。
原文摘要 · Abstract (English)
This paper analyzes Cross-Entropy (CE) loss in knowledge distillation (KD) for recommender systems. KD for recommender systems targets at distilling rankings, especially among items most likely to be preferred, and can only be computed on a small subset of items. Considering these features, we reveal the connection between CE loss and NDCG in the field of KD. We prove that when performing KD on an item subset, minimizing CE loss maximizes the lower bound of NDCG, only if an assumption of closure is satisfied. It requires that the item subset consists of the student's top items. However, this contradicts our goal of distilling rankings of the teacher's top items. We empirically demonstrate the vast gap between these two kinds of top items. To bridge the gap between our goal and theoretical support, we propose Rejuvenated Cross-Entropy for Knowledge Distillation (RCE-KD). It splits the top items given by the teacher into two subsets based on whether they are highly ranked by the student. For the subset that defies the condition, a sampling strategy is devised to use teacher-student collaboration to approximate our assumption of closure. We also combine the losses on the two subsets adaptively. Extensive experiments demonstrate the effectiveness of our method. Our code is available at https://github.com/BDML-lab/RCE-KD.
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