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View all- Zhu XFu JChen C(2023)Matrix Completion of Adaptive Jumping Graph Neural Networks for Recommendation SystemsIEEE Access10.1109/ACCESS.2023.330594511(88433-88450)Online publication date: 2023
Graph-Based Model-Agnostic Data Subsampling for Recommendation Systems
Authors: 
Xiaohui Chen, Jiankai Sun, Taiqing Wang, Ruocheng Guo, Li-Ping Liu, Aonan ZhangAuthors Info & Claims
Xiaohui Chen, Jiankai Sun, Taiqing Wang, Ruocheng Guo, Li-Ping Liu, Aonan ZhangAuthors Info & ClaimsPages 3865 - 3876
Abstract
Data subsampling is widely used to speed up the training of large-scale recommendation systems. Most subsampling methods are model-based and often require a pre-trained pilot model to measure data importance via e.g. sample hardness. However, when the pilot model is misspecified, model-based subsampling methods deteriorate. Since model misspecification is persistent in real recommendation systems, we instead propose model-agnostic data subsampling methods by only exploring input data structure represented by graphs. Specifically, we study the topology of the user-item graph to estimate the importance of each user-item interaction (an edge in the user-item graph) via graph conductance, followed by a propagation step on the network to smooth out the estimated importance value. Since our proposed method is model-agnostic, we can marry the merits of both model-agnostic and model-based subsampling methods. Empirically, we show that combing the two consistently improves over any single method on the used datasets. Experimental results on KuaiRec and MIND datasets demonstrate that our proposed methods achieve superior results compared to baseline approaches.
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Model-agnostic data subsampling method for recommendation system.
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Index Terms
- Graph-Based Model-Agnostic Data Subsampling for Recommendation Systems
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Information & Contributors
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Published In
August 2023
5996 pages
ISBN:9798400701030
DOI:10.1145/3580305
- General Chairs:
- Ambuj Singh,
- Yizhou Sun,
- Program Chairs:
- Leman Akoglu,
- Dimitrios Gunopulos,
- Xifeng Yan,
- Ravi Kumar,
- Fatma Ozcan,
- Jieping Ye
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Published: 04 August 2023
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KDD '23: The 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
August 6 - 10, 2023
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View all- Zhu XFu JChen C(2023)Matrix Completion of Adaptive Jumping Graph Neural Networks for Recommendation SystemsIEEE Access10.1109/ACCESS.2023.330594511(88433-88450)Online publication date: 2023
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