SLED: Structure Learning based Denoising for Recommendation
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Published:2023-11-08
Issue:2
Volume:42
Page:1-31
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ISSN:1046-8188
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Container-title:ACM Transactions on Information Systems
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language:en
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Short-container-title:ACM Trans. Inf. Syst.
Author:
Zhang Shengyu1ORCID,
Jiang Tan1ORCID,
Kuang Kun1ORCID,
Feng Fuli2ORCID,
Yu Jin3ORCID,
Ma Jianxin3ORCID,
Zhao Zhou1ORCID,
Zhu Jianke1ORCID,
Yang Hongxia3ORCID,
Chua Tat-Seng4ORCID,
Wu Fei1ORCID
Affiliation:
1. Zhejiang University, China
2. University of Science and Technology of China, China
3. Alibaba Group, China
4. National University of Singapore, Singapore
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.
Funder
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
Publisher
Association for Computing Machinery (ACM)
Subject
Computer Science Applications,General Business, Management and Accounting,Information Systems