arXiv cs.AI· Xinyu He, Jose Sepulveda, Mostafa Rahmani, Alyssa Woo, Fei Wang, Hanghang Tong·· 4 小时前AI 评分34
TintCF：利用时间信息为协同过滤去噪
Time Will Tell: Harness Time to Denoise Collaborative Filtering
AI 导读
TintCF 提出一种时间感知的用户/物品嵌入机制，结合全局偏好与时间模式来识别和纠正协同过滤中的噪声交互。该方法包含两种策略——重写邻接矩阵（Tint-A）与重写损失函数（Tint-L），针对隐式反馈数据和恶意交互导致的模型性能下降问题。在四个真实数据集上的实验验证了时间信息对去噪的有效性，代码已开源，论文被 IEEE BigData 2026 接收。
正文 · 原文
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 a significant challenge in collaborative filtering, especially for data collected from implicit feedback. Both noisy interactions and interactions crafted by malicious users could greatly degenerate model performance. To purify the input data and enhance model robustness, it is vital to understand how noisy interactions can be identified and rectified. Existing denoising methods focus on mining the correlation between user/item embeddings and noisy interactions, neglecting the temporal dimension for denoising. However, time information bears its natural and unique advantage in revealing noisy edges as we observe in this paper. In this work, we introduce a simple yet effective mechanism for generating time-aware user/item embeddings that capture both global preferences and temporal patterns. Two strategies, reweighting adjacency matrix (Tint-A) and reweighting loss function (Tint-L), are proposed to leverage the embeddings for the task of using \underline{t}ime \underline{in}forma\underline{t}ion to denoise \underline{c}ollaborative \underline{f}iltering (TintCF). Extensive experiments have been conducted on four real-world datasets to demonstrate the efficacy of our methods and illustrate how time information indeed helps identify and rectify noisy edges. Our codes are available at \href{this https URL}{this https URL}.
| Comments: | Accepted by IEEE BigData 2026 |
| Subjects: | Information Retrieval (cs.IR); Artificial Intelligence (cs.AI); Machine Learning (cs.LG) |
| Cite as: | arXiv:2411.09181 [cs.IR] |
| (or arXiv:2411.09181v2 [cs.IR] for this version) | |
| https://doi.org/10.48550/arXiv.2411.09181 arXiv-issued DOI via DataCite |
Submission history
From: Xinyu He [view email]
[v1]
Thu, 14 Nov 2024 04:39:30 UTC (134 KB)
[v2]
Fri, 30 Oct 2026 04:06:39 UTC (355 KB)
来源：arXiv cs.AI · arxiv.org