---
title: "Test-Time Adaptation on Recommender System with Data-Centric Graph Transformation"
authors:
  - "Yating Liu"
  - "Xin Zheng"
  - "Yi Li"
  - "Yanqing Guo"
doi: "10.24963/ijcai.2025/510"
url: "https://ijcai.org/proceedings/2025/510"
pdf: "https://ijcai.org/proceedings/2025/0510.pdf"
venue: "Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence"
year: 2025
pages: "4579-4587"
track: "Main Track"
keywords:
  - "Knowledge Representation and Reasoning: KRR: Learning and reasoning"
  - "Machine Learning: ML: Deep learning architectures"
  - "Data Mining: DM: Mining graphs"
---

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

Yating Liu, Xin Zheng, Yi Li, Yanqing Guo

*Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence*, pages 4579-4587. DOI: [10.24963/ijcai.2025/510](https://doi.org/10.24963/ijcai.2025/510)

## 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.