Behavior Importance-Aware Graph Neural Architecture Search for Cross-Domain Recommendation

Fuente: arXiv

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Bibliographic Details
Main Authors:	Ge, Chendi, Wang, Xin, Zhang, Ziwei, Qin, Yijian, Chen, Hong, Wu, Haiyang, Zhang, Yang, Yang, Yuekui, Zhu, Wenwu
Format:	Preprint
Published:	2025
Subjects:	Information Retrieval Machine Learning
Online Access:	Acceder al recurso
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author	Ge, Chendi Wang, Xin Zhang, Ziwei Qin, Yijian Chen, Hong Wu, Haiyang Zhang, Yang Yang, Yuekui Zhu, Wenwu
author_facet	Ge, Chendi Wang, Xin Zhang, Ziwei Qin, Yijian Chen, Hong Wu, Haiyang Zhang, Yang Yang, Yuekui Zhu, Wenwu
contents	Cross-domain recommendation (CDR) mitigates data sparsity and cold-start issues in recommendation systems. While recent CDR approaches using graph neural networks (GNNs) capture complex user-item interactions, they rely on manually designed architectures that are often suboptimal and labor-intensive. Additionally, extracting valuable behavioral information from source domains to improve target domain recommendations remains challenging. To address these challenges, we propose Behavior importance-aware Graph Neural Architecture Search (BiGNAS), a framework that jointly optimizes GNN architecture and data importance for CDR. BiGNAS introduces two key components: a Cross-Domain Customized Supernetwork and a Graph-Based Behavior Importance Perceptron. The supernetwork, as a one-shot, retrain-free module, automatically searches the optimal GNN architecture for each domain without the need for retraining. The perceptron uses auxiliary learning to dynamically assess the importance of source domain behaviors, thereby improving target domain recommendations. Extensive experiments on benchmark CDR datasets and a large-scale industry advertising dataset demonstrate that BiGNAS consistently outperforms state-of-the-art baselines. To the best of our knowledge, this is the first work to jointly optimize GNN architecture and behavior data importance for cross-domain recommendation.
format	Preprint
id	arxiv_https___arxiv_org_abs_2504_07102
institution	arXiv
publishDate	2025
record_format	arxiv
spellingShingle	Behavior Importance-Aware Graph Neural Architecture Search for Cross-Domain Recommendation Ge, Chendi Wang, Xin Zhang, Ziwei Qin, Yijian Chen, Hong Wu, Haiyang Zhang, Yang Yang, Yuekui Zhu, Wenwu Information Retrieval Machine Learning Cross-domain recommendation (CDR) mitigates data sparsity and cold-start issues in recommendation systems. While recent CDR approaches using graph neural networks (GNNs) capture complex user-item interactions, they rely on manually designed architectures that are often suboptimal and labor-intensive. Additionally, extracting valuable behavioral information from source domains to improve target domain recommendations remains challenging. To address these challenges, we propose Behavior importance-aware Graph Neural Architecture Search (BiGNAS), a framework that jointly optimizes GNN architecture and data importance for CDR. BiGNAS introduces two key components: a Cross-Domain Customized Supernetwork and a Graph-Based Behavior Importance Perceptron. The supernetwork, as a one-shot, retrain-free module, automatically searches the optimal GNN architecture for each domain without the need for retraining. The perceptron uses auxiliary learning to dynamically assess the importance of source domain behaviors, thereby improving target domain recommendations. Extensive experiments on benchmark CDR datasets and a large-scale industry advertising dataset demonstrate that BiGNAS consistently outperforms state-of-the-art baselines. To the best of our knowledge, this is the first work to jointly optimize GNN architecture and behavior data importance for cross-domain recommendation.
title	Behavior Importance-Aware Graph Neural Architecture Search for Cross-Domain Recommendation
topic	Information Retrieval Machine Learning
url	https://arxiv.org/abs/2504.07102