让推荐系统跨数据集零样本推理，无需重新训练
Inductive Transfer Learning for Graph-Based Recommenders
- 动态计算节点嵌入，实现跨数据集的归纳迁移
- 零样本下性能媲美传统方法，微调后进一步提升
- 适合新用户新物品场景，无需用户物品对齐
基于图的推荐系统通常在直接设定下训练，限制了其在新用户、新物品或新数据集上的应用。我们提出NBF-Rec，一种支持跨数据集归纳迁移学习的图推荐模型。与依赖嵌入的传统方法不同，NBF-Rec在推理时动态计算节点嵌入。我们在七个真实数据集（涵盖电影、音乐、电商和位置签到）上评估该方法。NBF-Rec在零样本设置下表现优异，无需目标域训练数据；通过轻量级微调可进一步提升性能。结果表明，图推荐中的归纳迁移是可行的，且交互级消息传递可在无需对齐用户或物品的情况下实现跨数据集泛化。
原文摘要 · Abstract (English)
Graph-based recommender systems are commonly trained in transductive settings, which limits their applicability to new users, items, or datasets. We propose NBF-Rec, a graph-based recommendation model that supports inductive transfer learning across datasets with disjoint user and item sets. Unlike conventional embedding-based methods that require retraining for each domain, NBF-Rec computes node embeddings dynamically at inference time. We evaluate the method on seven real-world datasets spanning movies, music, e-commerce, and location check-ins. NBF-Rec achieves competitive performance in zero-shot settings, where no target domain data is used for training, and demonstrates further improvements through lightweight fine-tuning. These results show that inductive transfer is feasible in graph-based recommendation and that interaction-level message passing supports generalization across datasets without requiring aligned users or items.
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