Inductive Transfer Learning for Graph-Based Recommenders
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866917044602011648 |
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| author | Grötschla, Florian Trachsel, Elia Lanzendörfer, Luca A. Wattenhofer, Roger |
| author_facet | Grötschla, Florian Trachsel, Elia Lanzendörfer, Luca A. Wattenhofer, Roger |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_22799 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Inductive Transfer Learning for Graph-Based Recommenders Grötschla, Florian Trachsel, Elia Lanzendörfer, Luca A. Wattenhofer, Roger Machine Learning 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. |
| title | Inductive Transfer Learning for Graph-Based Recommenders |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2510.22799 |