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journal article Nov 04, 2024

AdaGIN: Adaptive Graph Interaction Network for Click-Through Rate Prediction

Lei Sang ORCID Honghao Li ORCID Yiwen Zhang ORCID Yun Yang ORCID
ACM Transactions on Information Systems Vol. 43 No. 1 pp. 1-31 · Association for Computing Machinery (ACM)
View at Publisher Save 10.1145/3681785
Abstract
The goal of click-through rate (CTR) prediction in recommender systems is to effectively work with input features. However, existing CTR prediction models face three main issues. First, many models use a basic approach for feature combinations, leading to noise and reduced accuracy. Second, there is no consideration for the varying importance of features in different interaction orders, affecting model performance. Third, current model architectures struggle to capture different interaction signals from various semantic spaces, leading to sub-optimal performance. To address these issues, we propose the Adaptive Graph Interaction Network (AdaGIN) with the Graph Neural Networks-based Feature Interaction Module (GFIM), the Multi-semantic Feature Interaction Module (MFIM), and the Negative Feedback-based Search (NFS) algorithm. GFIM explicitly aggregates information between features and assesses their importance, while MFIM captures information from different semantic spaces. NFS uses negative feedback to optimize model complexity. Experimental results show AdaGIN outperforms existing models on large-scale public benchmark datasets.
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Metrics
10
Citations
72
References
Details
Published
Nov 04, 2024
Vol/Issue
43(1)
Pages
1-31
License
View
Authors
L
Lei Sang

Anhui University, Hefei, China

H
Honghao Li

Anhui University, Hefei, China

Y
Yiwen Zhang

Anhui University, Hefei, China

Y
Yun Yang

Swinburne University of Technology, Hawthorn, Melbourne, Australia

Funding
National Science Foundation of China Award: 62272001 and 62206002
Hefei Key Common Technology Project Award: GJ2022GX15
Cite This Article
Lei Sang, Honghao Li, Yiwen Zhang, et al. (2024). AdaGIN: Adaptive Graph Interaction Network for Click-Through Rate Prediction. ACM Transactions on Information Systems, 43(1), 1-31. https://doi.org/10.1145/3681785
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