Self-guided learning to denoise for robust recommendation
The ubiquity of implicit feedback makes them the default choice to build modern recommender systems. Generally speaking, observed interactions are considered as positive samples, while unobserved interactions are considered as negative ones. However, implicit feedback is inherently noisy because of...
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sg-smu-ink.sis_research-81852023-08-04T02:44:33Z Self-guided learning to denoise for robust recommendation GAO, Yunjun DU, Yuntao HU, Yujia CHEN, Lu ZHU, Xinjun FANG, Ziquan ZHENG, Baihua The ubiquity of implicit feedback makes them the default choice to build modern recommender systems. Generally speaking, observed interactions are considered as positive samples, while unobserved interactions are considered as negative ones. However, implicit feedback is inherently noisy because of the ubiquitous presence of noisy-positive and noisy-negative interactions. Recently, some studies have noticed the importance of denoising implicit feedback for recommendations, and enhanced the robustness of recommendation models to some extent. Nonetheless, they typically fail to (1) capture the hard yet clean interactions for learning comprehensive user preference, and (2) provide a universal denoising solution that can be applied to various kinds of recommendation models. In this paper, we thoroughly investigate the memorization effect of recommendation models, and propose a new denoising paradigm, i.e., Self-Guided Denoising Learning (SGDL), which is able to collect memorized interactions at the early stage of the training (i.e., “noise-resistant” period), and leverage those data as denoising signals to guide the following training (i.e., “noise-sensitive” period) of the model in a meta-learning manner. Besides, our method can automatically switch its learning phase at the memorization point from memorization to self-guided learning, and select clean and informative memorized data via a novel adaptive denoising scheduler to improve the robustness. We incorporate SGDL with four representative recommendation models (i.e., NeuMF, CDAE, NGCF and LightGCN) and different ranking loss functions. The experimental results on three benchmark datasets demonstrate the effectiveness of SGDL over the state-of-the-art denoising methods like T-CE, IR, DeCA, and even state-of-the-art robust graph-based methods like SGCN and SGL. 2022-07-01T07:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/7182 info:doi/10.1145/3477495.3532059 https://ink.library.smu.edu.sg/context/sis_research/article/8185/viewcontent/_Submit__Self_Guided_Learning_to_Denoise_for_Robust_Recommendation.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University Denoising Recommendation Implicit Feedback Robust Learning Databases and Information Systems |
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Denoising Recommendation Implicit Feedback Robust Learning Databases and Information Systems GAO, Yunjun DU, Yuntao HU, Yujia CHEN, Lu ZHU, Xinjun FANG, Ziquan ZHENG, Baihua Self-guided learning to denoise for robust recommendation |
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The ubiquity of implicit feedback makes them the default choice to build modern recommender systems. Generally speaking, observed interactions are considered as positive samples, while unobserved interactions are considered as negative ones. However, implicit feedback is inherently noisy because of the ubiquitous presence of noisy-positive and noisy-negative interactions. Recently, some studies have noticed the importance of denoising implicit feedback for recommendations, and enhanced the robustness of recommendation models to some extent. Nonetheless, they typically fail to (1) capture the hard yet clean interactions for learning comprehensive user preference, and (2) provide a universal denoising solution that can be applied to various kinds of recommendation models. In this paper, we thoroughly investigate the memorization effect of recommendation models, and propose a new denoising paradigm, i.e., Self-Guided Denoising Learning (SGDL), which is able to collect memorized interactions at the early stage of the training (i.e., “noise-resistant” period), and leverage those data as denoising signals to guide the following training (i.e., “noise-sensitive” period) of the model in a meta-learning manner. Besides, our method can automatically switch its learning phase at the memorization point from memorization to self-guided learning, and select clean and informative memorized data via a novel adaptive denoising scheduler to improve the robustness. We incorporate SGDL with four representative recommendation models (i.e., NeuMF, CDAE, NGCF and LightGCN) and different ranking loss functions. The experimental results on three benchmark datasets demonstrate the effectiveness of SGDL over the state-of-the-art denoising methods like T-CE, IR, DeCA, and even state-of-the-art robust graph-based methods like SGCN and SGL. |
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GAO, Yunjun DU, Yuntao HU, Yujia CHEN, Lu ZHU, Xinjun FANG, Ziquan ZHENG, Baihua |
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GAO, Yunjun DU, Yuntao HU, Yujia CHEN, Lu ZHU, Xinjun FANG, Ziquan ZHENG, Baihua |
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GAO, Yunjun |
title |
Self-guided learning to denoise for robust recommendation |
title_short |
Self-guided learning to denoise for robust recommendation |
title_full |
Self-guided learning to denoise for robust recommendation |
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Self-guided learning to denoise for robust recommendation |
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Self-guided learning to denoise for robust recommendation |
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self-guided learning to denoise for robust recommendation |
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Institutional Knowledge at Singapore Management University |
publishDate |
2022 |
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https://ink.library.smu.edu.sg/sis_research/7182 https://ink.library.smu.edu.sg/context/sis_research/article/8185/viewcontent/_Submit__Self_Guided_Learning_to_Denoise_for_Robust_Recommendation.pdf |
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