Proceedings

Test-Time Adaptation on Recommender System with Data-Centric Graph Transformation

Yating Liu, Xin Zheng, Yi Li, Yanqing Guo

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Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence. Main Track. Pages 4579-4587.

https://doi.org/10.24963/ijcai.2025/510

Keywords

  • Knowledge Representation and Reasoning: KRR: Learning and reasoning
  • Machine Learning: ML: Deep learning architectures
  • Data Mining: DM: Mining graphs

Abstract

Distribution shifts in recommender systems between training and testing in user-item interactions lead to inaccurate recommendations. Despite the promising performance of test-time adaptation technology in various domains, it still faces challenges in recommender systems due to the impracticality of fine-tuning models and the infeasibility of obtaining test-time labels. To address these challenges, we first propose a Test-Time Adaptation framework for Graph-based Recommender system, named TTA-GREC, to dynamically adapt user-item graphs at test time in a data-centric way, handling distribution shifts effectively. Specifically, our TTA-GREC targets KG-enhanced GNN-based recommender systems with three core components: (1) Pseudo-label guided UI graph transformation for adaptive improvement; (2) Rationale score guided KG graph revision for semantic enhancement; and (3) Sampling-based self-supervised adaptation for contrastive learning. Experiments demonstrate TTA-GREC's superiority at test time and provide new data-centric insights on test-time adaptation for better recommender system inference.

Yating Liu, Xin Zheng, Yi Li, and Yanqing Guo. 2025. Test-Time Adaptation on Recommender System with Data-Centric Graph Transformation. In Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence, IJCAI-25. IJCAI, 4579–4587. https://doi.org/10.24963/ijcai.2025/510
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