GOD：通过深度嫁接提升序列推荐的泛化能力
GOD: Enhancing Generalization via Deep Grafting for Sequential Recommendation
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中文总结 AI 辅助
本文提出组件级蒸馏框架GOD，通过嫁接教师与学生模型组件提供组件级反馈，仅用学生模型推理，在三个真实数据集上较最优基线最高提升13.92%，解决序列推荐泛化能力弱的问题。
中文摘要 AI 辅助
序列推荐系统常受稀疏且含噪声的历史数据困扰，限制了对未见过交互的泛化能力。知识蒸馏通过将教师模型的密集监督转移到学生模型来缓解这一问题，但多数蒸馏方法独立运行教师与学生模型，再将学生的输出或表示与教师匹配，这类监督会混淆学生组件的影响，难以判断泛化能力弱是源于不可靠的嵌入、过拟合的编码还是对稀疏历史的协同适应。本文提出Graft-Oriented Distillation（GOD，面向嫁接的蒸馏），这是一种组件级蒸馏框架，通过嫁接提升泛化能力。嫁接指将选定的冻结教师组件替换为可训练的学生对应组件，构建混合源模型；GOD利用这些混合模型，通过教师编码器评估学生嵌入、通过学生编码器评估教师嵌入，提供组件级反馈；推理时仅使用学生模型，无额外开销。在三个真实世界数据集上，GOD的性能优于现有最优基线，最高提升13.92%。
英文摘要
Sequential recommenders often struggle with sparse and noisy histories, limiting generalization to unseen interactions. Knowledge distillation mitigates this by transferring dense supervision from a teacher to a student. However, most distillation methods run teacher and student independently, then match student outputs or representations to the teacher. Such supervision entangles student-component effects, blurring whether weak generalization stems from unreliable embeddings, overfitted encoding, or co-adaptation to sparse histories. In this paper, we propose Graft-Oriented Distillation (GOD), a component-level distillation framework for improved generalization through grafting. Grafting denotes replacing selected frozen-teacher components with trainable student counterparts to build hybrid source models. GOD uses these hybrid models to evaluate student embeddings with the teacher encoder and the student encoder with teacher embeddings, providing component-level feedback. At inference, GOD uses only the student, incurring no additional cost. Across three real-world datasets, GOD outperforms state-of-the-art baselines by up to 13.92%.
发表机构
- Pohang University of Science and Technology（浦项科技大学）
机构由 AI 辅助整理，请以论文原文为准。
