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.
@article{arxiv.2510.22799,
title = {Inductive Transfer Learning for Graph-Based Recommenders},
author = {Florian Grötschla and Elia Trachsel and Luca A. Lanzendörfer and Roger Wattenhofer},
journal= {arXiv preprint arXiv:2510.22799},
year = {2025}
}
Comments
Accepted at the New Perspectives in Graph Machine Learning Workshop at NeurIPS 2025