English(EN) Time Will Tell: Harness Time to Denoise Collaborative Filtering

新方法利用时间来去噪协同过滤模型

作者 PulseAugur 编辑部 · [1 个来源] · 2026-11-02 05:00

研究人员开发了一种名为TintCF的新方法，该方法利用时间信息来去噪协同过滤模型。这种方法解决了隐式反馈数据中噪声交互的挑战，这些噪声交互会降低推荐系统的性能。TintCF生成时间感知的用户和项目嵌入，以识别和纠正这些噪声边，并提出了两种策略：重新加权邻接矩阵或损失函数。在真实数据集上的实验表明，使用时间信息可以提高模型鲁棒性。 AI

影响这项研究可以通过减轻噪声用户-项目交互数据的影响来提高推荐系统的准确性和鲁棒性。

排序理由学术论文，详细介绍了一种用于去噪协同过滤模型的新方法。[lever_c_demoted from research: ic=1 ai=1.0]

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报道来源 [1]

arXiv cs.LG TIER_1 English(EN) · Xinyu He, Jose Sepulveda, Mostafa Rahmani, Alyssa Woo, Fei Wang, Hanghang Tong · 2026-11-02 05:00

Time Will Tell: Harness Time to Denoise Collaborative Filtering

arXiv:2411.09181v2 Announce Type: replace-cross Abstract: Neural graph collaborative filtering is a powerful tool for recommendation due to its superb capability of capturing the user-item interaction. Despite the substantial progress, the noisy nature of interaction data remains…

本文如何被排名

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Time Will Tell: Harness Time to Denoise Collaborative Filtering

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