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29 November 2023 Meta-learning-based recommendation method for self-supervised hybrid comparison learning
Huixin Jiang, Lingyu Yan, Donghua Liu, Xiang Wan
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Proceedings Volume 12937, International Conference on Internet of Things and Machine Learning (IoTML 2023); 129371M (2023) https://doi.org/10.1117/12.3013361
Event: International Conference on Internet of Things and Machine Learning (IoTML 2023), 2023, Singapore, Singapore
Abstract
A self-supervised mixed comparison recommendation method based on meta-learning (MSHCL) is proposed to address the problem of poor recommendation accuracy in social recommendation algorithms. Collaborative filtering based on graph neural networks can model the inter-node interactions between users and items, and make effective use of higherorder neighbor information. However, its representation is very susceptible to interaction noise, and thus the great potential of node-level information is not well utilized. To address the above issues, We first learn the embedding of nodes through the network view and meta-path view to fully capture the heterogeneous network structure. In addition, self-supervised learning as a novel learning method using unlabeled data effectively mitigates the data sparsity problem, which in turn fuses self-supervised learning into hybrid contrast learning model training. We have done empirical and ablation studies on a real dataset to demonstrate that the MSHCL model outperforms the current mainstream methods.
(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.
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Huixin Jiang, Lingyu Yan, Donghua Liu, and Xiang Wan "Meta-learning-based recommendation method for self-supervised hybrid comparison learning", Proc. SPIE 12937, International Conference on Internet of Things and Machine Learning (IoTML 2023), 129371M (29 November 2023); https://doi.org/10.1117/12.3013361
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KEYWORDS
Data modeling
Ablation
Neural networks
Systems modeling
Statistical analysis
Target acquisition
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