{"type":"ScholarlyArticle","title":"Test-Time Adaptation on Recommender System with Data-Centric Graph Transformation","authors":[{"name":"Yating Liu","slug":"yating-liu","given":"Yating","family":"Liu","affiliation":""},{"name":"Xin Zheng","slug":"xin-zheng","given":"Xin","family":"Zheng","affiliation":""},{"name":"Yi Li","slug":"yi-li","given":"Yi","family":"Li","affiliation":""},{"name":"Yanqing Guo","slug":"yanqing-guo","given":"Yanqing","family":"Guo","affiliation":""}],"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.","doi":"10.24963/ijcai.2025/510","url":"https://ijcai.org/proceedings/2025/510","pdf":"https://ijcai.org/proceedings/2025/0510.pdf","markdown":"https://ijcai.org/proceedings/2025/510.md","bibtex":"https://ijcai.org/proceedings/2025/bibtex/510","volume":{"slug":"2025","title":"Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2025","url":"https://ijcai.org/proceedings/2025/","year":2025,"location":"Montreal, Canada","dates":"16-22 August 2025","editors":"James Kwok","isbn":"978-1-956792-06-5","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2025"},"section":"Knowledge Representation and Reasoning","track":"Main Track","number":510,"pages":{"first":4579,"last":4587},"keywords":["Knowledge Representation and Reasoning: KRR: Learning and reasoning","Machine Learning: ML: Deep learning architectures","Data Mining: DM: Mining graphs"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}