LinkFND: Simple Framework for False Negative Detection in Recommendation Tasks With Graph Contrastive Learning
Self-supervised learning has been shown to be effective in various fields, proving its usefulness in contrastive learning. Recently, graph contrastive learning has shown state-of-the-art performance in the recommendation task. They created two views and learned node embeddings so that target nodes i...
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| Format: | Article |
| Language: | English |
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IEEE
2023-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10368024/ |
| _version_ | 1797373116685484032 |
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| author | Sanghun Kim Hyeryung Jang |
| author_facet | Sanghun Kim Hyeryung Jang |
| author_sort | Sanghun Kim |
| collection | DOAJ |
| description | Self-supervised learning has been shown to be effective in various fields, proving its usefulness in contrastive learning. Recently, graph contrastive learning has shown state-of-the-art performance in the recommendation task. They created two views and learned node embeddings so that target nodes in the two views attract each other based on the target node, and non-target nodes in the two views repel each other. However, they overlooked the fact that false negatives can occur when negative pairs are repelled. It has been shown through various studies that false negatives in contrastive learning in various fields can have a negative impact on model training, but research on the impact of false negatives in link prediction tasks, such as recommendation tasks, where classes cannot be clearly defined, is still hardly explored. In this paper, we propose an approach to define false negatives in link prediction tasks and fully utilize them in learning. Learning by defining false negatives and removing them from negative pairs showed consistent improvements over existing graph contrastive learning on five benchmark datasets. In addition, we found through comprehensive experimental studies that learning by removing false negatives is of great advantage, especially for low-density datasets. On top of these advantages, our false negative detection and false negative elimination can be naturally integrated into any graph contrastive learning architecture. |
| first_indexed | 2024-03-08T18:45:45Z |
| format | Article |
| id | doaj.art-23fc343ac8234b50a3e6a8291620c09d |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-03-08T18:45:45Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-23fc343ac8234b50a3e6a8291620c09d2023-12-29T00:03:30ZengIEEEIEEE Access2169-35362023-01-011114530814531910.1109/ACCESS.2023.334533810368024LinkFND: Simple Framework for False Negative Detection in Recommendation Tasks With Graph Contrastive LearningSanghun Kim0https://orcid.org/0009-0001-6351-4055Hyeryung Jang1https://orcid.org/0000-0002-7314-0739Department of Artificial Intelligence, Dongguk University, Seoul, South KoreaDepartment of Artificial Intelligence, Dongguk University, Seoul, South KoreaSelf-supervised learning has been shown to be effective in various fields, proving its usefulness in contrastive learning. Recently, graph contrastive learning has shown state-of-the-art performance in the recommendation task. They created two views and learned node embeddings so that target nodes in the two views attract each other based on the target node, and non-target nodes in the two views repel each other. However, they overlooked the fact that false negatives can occur when negative pairs are repelled. It has been shown through various studies that false negatives in contrastive learning in various fields can have a negative impact on model training, but research on the impact of false negatives in link prediction tasks, such as recommendation tasks, where classes cannot be clearly defined, is still hardly explored. In this paper, we propose an approach to define false negatives in link prediction tasks and fully utilize them in learning. Learning by defining false negatives and removing them from negative pairs showed consistent improvements over existing graph contrastive learning on five benchmark datasets. In addition, we found through comprehensive experimental studies that learning by removing false negatives is of great advantage, especially for low-density datasets. On top of these advantages, our false negative detection and false negative elimination can be naturally integrated into any graph contrastive learning architecture.https://ieeexplore.ieee.org/document/10368024/False negativegraph contrastive learningrecommendation tasksself-supervised learning |
| spellingShingle | Sanghun Kim Hyeryung Jang LinkFND: Simple Framework for False Negative Detection in Recommendation Tasks With Graph Contrastive Learning IEEE Access False negative graph contrastive learning recommendation tasks self-supervised learning |
| title | LinkFND: Simple Framework for False Negative Detection in Recommendation Tasks With Graph Contrastive Learning |
| title_full | LinkFND: Simple Framework for False Negative Detection in Recommendation Tasks With Graph Contrastive Learning |
| title_fullStr | LinkFND: Simple Framework for False Negative Detection in Recommendation Tasks With Graph Contrastive Learning |
| title_full_unstemmed | LinkFND: Simple Framework for False Negative Detection in Recommendation Tasks With Graph Contrastive Learning |
| title_short | LinkFND: Simple Framework for False Negative Detection in Recommendation Tasks With Graph Contrastive Learning |
| title_sort | linkfnd simple framework for false negative detection in recommendation tasks with graph contrastive learning |
| topic | False negative graph contrastive learning recommendation tasks self-supervised learning |
| url | https://ieeexplore.ieee.org/document/10368024/ |
| work_keys_str_mv | AT sanghunkim linkfndsimpleframeworkforfalsenegativedetectioninrecommendationtaskswithgraphcontrastivelearning AT hyeryungjang linkfndsimpleframeworkforfalsenegativedetectioninrecommendationtaskswithgraphcontrastivelearning |