Inductive Transfer Learning for Graph-Based Recommenders

Florian Grötschla, Elia Trachsel, Luca A Lanzendörfer, Roger Wattenhofer

Keywords: graph neural networks, recommender systems, transfer learning

TL;DR: We show the effectiveness of transfer learning for graph-based recommender systems.

Abstract:

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.

Submission Number: 65