Adaptive multi-view comparison recommendation based on MOE

Jia Zelu; Yu Jiaheng; Wen Wanglong; Xiao Yafu; Zhu Kai; Li Jing

doi:10.11959/j.issn.2096-0271.2026064

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Adaptive multi-view comparison recommendation based on MOE

更新时间：2026-08-12

- Adaptive multi-view comparison recommendation based on MOE
- BIG DATA RESEARCH Pages: 1-16(2026)
- 作者机构：
  
  1.深圳市宝安区政务服务和数据管理局，广东深圳 518101
  2.中冶南方工程技术有限公司，湖北武汉 430223
  3.武汉大学计算机学院，湖北武汉 430072
- 作者简介：
- 基金信息：
  
  The National Natural Science Foundation of China(62372335)
- DOI：10.11959/j.issn.2096-0271.2026064
  CLC： TP182
- Received：30 December 2056，
  
  Online First：23 April 2026，
- 稿件说明：
移动端阅览
贾泽露,于佳恒,温旺龙等.基于MOE的自适应多视图对比推荐[J].大数据,

Jia Zelu,Yu Jiaheng,Wen Wanglong,et al.Adaptive multi-view comparison recommendation based on MOE[J].BIG DATA RESEARCH,
贾泽露,于佳恒,温旺龙等.基于MOE的自适应多视图对比推荐[J].大数据, DOI：10.11959/j.issn.2096-0271.2026064.

Jia Zelu,Yu Jiaheng,Wen Wanglong,et al.Adaptive multi-view comparison recommendation based on MOE[J].BIG DATA RESEARCH, DOI：10.11959/j.issn.2096-0271.2026064.

摘要

近年来，图对比学习在推荐系统中的应用取得了显著进展。然而，当前大多数图增强策略仍依赖人工经验设计，缺乏灵活性与泛化能力，难以适应多样化的推荐任务和复杂的图结构。为此，提出一种基于混合专家模型的自适应多视图对比推荐算法（MAMGCL算法）。该算法构建包含多种增强策略的专家池，并引入双路门控机制实现用户侧与物品侧的专家动态融合，进一步生成差异化增强视图，并利用多分支图卷积网络实现节点嵌入表达，最后结合子视图级对比学习进行优化。实验在3个真实数据集上展开，验证了所提出方法在推荐性能和鲁棒性方面均优于现有主流对比推荐模型，展示了较强的泛化能力与应用潜力。

Abstract

In recent years

the application of graph contrastive learning in recommendation systems has made significant progress. However

most current graph enhancement strategies still rely on manual experience design

lack flexibility and generalization ability

and are difficult to adapt to diverse recommendation tasks and complex graph structures. To this end

this paper proposes an adaptive multi-view contrastive recommendation algorithm (MAMGCL) based on a hybrid expert model. This method constructs an expert pool containing multiple enhancement strategies

introduces a two-way gating mechanism to achieve dynamic fusion of experts on the user side and the item side

further generates differentiated enhanced views

uses a multi-branch graph convolutional network to achieve node embedding expression

and finally combines sub-view-level contrastive learning for optimization. Experiments are carried out on three real-world datasets

verifying that the proposed method is superior to the existing mainstream contrastive recommendation models in terms of recommendation performance and robustness

demonstrating strong generalization ability and application potential.

关键词

Keywords

references

刘天航 , 杨晓雪 , 周慧 , 等 . 基于图神经网络的协同过滤推荐算法综述 [J ] . 集成技术 , 2024 , 13 ( 4 ): 1 - 15 .

Liu T H , Yang X X , Zhou H , et al . A survey of collaborative filtering recommender algorithms based on graph neural networks [J ] . Journal of Integration Technology , 2024 , 13 ( 4 ): 1 - 15 .

杨娟 , 沈游人 . 图模融合: 人工智能系统事实表达和逻辑推理增强 [J ] . 大数据 , 2025 , 11 ( 1 ): 175 - 190 .

Yang J , Shen Y R . Graph-LLM fusion: enhancing fact representation and logical reasoning in artificial intelligence systems [J ] . Big Data Research , 2025 , 11 ( 1 ): 175 - 190 .

Luo D S , Cheng W , Yu W C , et al . Learning to drop: robust graph neural network via topological denoising [C ] // Proceedings of the 14th ACM International Conference on Web Search and Data Mining . New York : ACM , 2021 : 779 - 787 .

Wu J C , Wang X , Feng F L , et al . Self-supervised graph learning for recommendation [C ] // Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval . New York : ACM , 2021 : 726 - 735 .

Cai X , Huang C , Xia L , et al . Lightgcl: simple yet effective graph contrastive learning for recommendation [C ] // The 11th International Conference on Learning Representations . [ S.l. : s.n. ] , 2023 .

Ye H B , Li X J , Yao Y , et al . Towards robust neural graph collaborative filtering via structure denoising and embedding perturbation [J ] . ACM Transactions on Information Systems , 2023 , 41 ( 3 ): 1 - 28 .

Yu J L , Yin H Z , Xia X , et al . Are graph augmentations necessary? : simple graph contrastive learning for recommendation [C ] // Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval . New York : ACM , 2022 : 1294 - 1303 .

Sun J N , Zhang Y X , Guo W , et al . Neighbor interaction aware graph convolution networks for recommendation [C ] // Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval . New York : ACM , 2020 : 1289 - 1298 .

Yu J L , Xia X , Chen T , et al . XSimGCL: towards extremely simple graph contrastive learning for recommendation [J ] . IEEE Transactions on Knowledge and Data Engineering , 2024 , 36 ( 2 ): 913 - 926 .

Xu Z T , Chen S , Pan W K , et al . A multi-view graph contrastive learning framework for cross-domain sequential recommendation [J ] . ACM Transactions on Recommender Systems , 2025 , 3 ( 4 ): 1 - 28 .

Shazeer N , Mirhoseini A , Maziarz K , et al . Outrageously large neural networks: the sparsely-gated mixture-of-experts layer [PP ] . V1. arXiv ( 2017-01-23 )[ 2025-12-30 ] . arXiv: 1701.06538 .

刘天扬 , 寇思佳 , 金旭 , 等 . 基于图检索增强生成和少样本学习的美术作品鉴赏 [J ] . 大数据 , 2025 , 11 ( 5 ): 101 - 116 .

Liu T Y , Kou S J , Jin X , et al . Art appreciation based on graph retrieval augmented generation and few-shot learning [J ] . Big Data Research , 2025 , 11 ( 5 ): 101 - 116 .

Yang M D , Yuan Y , Zhou J , et al . Adaptive fusion of multi-view for graph contrastive recommendation [C ] // Proceedings of the 18th ACM Conference on Recommender Systems . New York : ACM , 2024 : 228 - 237 .

Wang X , He X N , Wang M , et al . Neural graph collaborative filtering [C ] // Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval . New York : ACM , 2019 : 165 - 174 .

Yao T S , Yi X Y , Cheng D Z , et al . Self-supervised learning for large-scale item recommendations [C ] // Proceedings of the 30th ACM International Conference on Information & Knowledge Management . New York : ACM , 2021 : 4321 - 4330 .

Xia L H , Huang C , Xu Y , et al . Hypergraph contrastive collaborative filtering [C ] // Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval . New York : ACM , 2022 : 70 - 79 .

Xia L H , Huang C , Zhang C X . Self-supervised hypergraph transformer for recommender systems [C ] // Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining . New York : ACM , 2022 : 2100 - 2109 .

Wu W , Wang C , Shen D Z , et al . AFDGCF: adaptive feature de-correlation graph collaborative filtering for recommendations [C ] // Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval . New York : ACM , 2024 : 1242 - 1252 .

Yu J H , Li J , He Y , et al . Bilateral unsymmetrical graph contrastive learning for recommendation [C ] // Proceedings of the 2024 International Joint Conference on Neural Networks . Piscataway : IEEE Press , 2024 : 1 - 8 .

Veličković P , Cucurull G , Casanova A , et al . Graph attention networks [C ] // International Conference on Learning Representations . [ S.l. : s.n. ] , 2018 .

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