Graph Collaborative Signals Denoising and Augmentation for Recommendation-Reference-Cited by-同舟云学术

Graph Collaborative Signals Denoising and Augmentation for Recommendation

Published:2023-07-18 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval
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Author:

Fan Ziwei¹^ORCID,Xu Ke¹^ORCID,Dong Zhang²^ORCID,Peng Hao³^ORCID,Zhang Jiawei⁴^ORCID,Yu Philip S.¹^ORCID

Affiliation:

1. University of Illinois at Chicago, Chicago, IL, USA

2. Amazon Ads, New York City, NY, USA

3. Beihang University, Beijing, China

4. University of California, Davis, Davis, CA, USA

Funder

NSF (National Science Foundation)

Natural Science Foundation of Beijing Municipality

S&T Program of Hebei

NSFC

National Key R&D Program of China

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3539618.3591994

Reference37 articles.

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2. A Generic Coordinate Descent Framework for Learning from Implicit Feedback

3. Rianne van den Berg , Thomas N Kipf , and Max Welling . 2018 . Graph convolutional matrix completion . In Proceedings of the Deep Learning Day@ACM SIGKDD. Rianne van den Berg, Thomas N Kipf, and Max Welling. 2018. Graph convolutional matrix completion. In Proceedings of the Deep Learning Day@ACM SIGKDD.

4. Measuring and Relieving the Over-Smoothing Problem for Graph Neural Networks from the Topological View

5. Structured Graph Convolutional Networks with Stochastic Masks for Recommender Systems

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