Cited By
View all- Sang LLi HZhang YZhang YYang Y(2024)AdaGIN: Adaptive Graph Interaction Network for Click-Through Rate PredictionACM Transactions on Information Systems10.1145/3681785Online publication date: 25-Jul-2024
SLED: Structure Learning based Denoising for Recommendation
Authors: 
Shengyu Zhang, Tan Jiang, Kun Kuang, Fuli Feng, Jin Yu, Jianxin Ma, Zhou Zhao, Jianke Zhu, Hongxia Yang, Tat-Seng Chua, Fei WuAuthors Info & Claims
Shengyu Zhang, Tan Jiang, Kun Kuang, Fuli Feng, Jin Yu, Jianxin Ma, Zhou Zhao, Jianke Zhu, Hongxia Yang, Tat-Seng Chua, Fei WuAuthors Info & ClaimsArticle No.: 43, Pages 1 - 31
Abstract
In recommender systems, click behaviors play a fundamental role in mining users’ interests and training models (clicked items as positive samples). Such signals are implicit feedback and are arguably less representative of users’ inherent interests. Most existing works denoise implicit feedback by introducing external signals, such as gaze, dwell time, and “like” behaviors. However, such explicit feedback is not always routinely available, or might be problematic to collect on a large scale. In this paper, we identify that an interaction’s related structural patterns in its neighborhood graph are potentially correlated with some outcome of implicit feedback (i.e., users’ ratings after consuming items), analogous to findings in other domains such as social networks. Inspired by this finding, we propose a novel Structure LEarning based Denoising (SLED) framework for denoising recommendation without explicit signals, which consists of two phases: center-aware graph structure learning and denoised recommendation. Phase 1 pre-trains a structural encoder in a self-supervised manner and learns to capture an interaction’s related structural patterns in its neighborhood graph. Phase 2 transfers the structure encoder to downstream recommendation datasets, which helps to down-weight the effect of noisy interactions on user interest modeling and loss calculation. We collect a relatively noisy industrial dataset across several days during a period of product promotion festival. Extensive experiments on this dataset and multiple public datasets demonstrate that the proposed SLED framework can significantly improve the recommendation quality over various base recommendation models.
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Index Terms
- SLED: Structure Learning based Denoising for Recommendation
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March 2024
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Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 08 November 2023
Online AM: 05 August 2023
Accepted: 13 July 2023
Revised: 06 June 2023
Received: 12 May 2022
Published in TOIS Volume 42, Issue 2
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- Research-article
Funding Sources
- Key R & D Projects of the Ministry of Science and Technology
- National Natural Science Foundation of China
- Young Elite Scientists Sponsorship Program by CAST
- Zhejiang Province Natural Science Foundation
- Project by Shanghai AI Laboratory
- Program of Zhejiang Province Science and Technology
- StarryNight Science Fund of Zhejiang University Shanghai Institute for Advanced Study
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Cited By
View all- Sang LLi HZhang YZhang YYang Y(2024)AdaGIN: Adaptive Graph Interaction Network for Click-Through Rate PredictionACM Transactions on Information Systems10.1145/3681785Online publication date: 25-Jul-2024
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