Graph Contrastive Learning for Optimizing Sparse Data in Recommender Systems with LightGCL
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arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866910977546518528 |
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| author | Jatavallabha, Aravinda Bharadwaj, Prabhanjan Chander, Ashish |
| author_facet | Jatavallabha, Aravinda Bharadwaj, Prabhanjan Chander, Ashish |
| contents | Graph Neural Networks (GNNs) are powerful tools for recommendation systems, but they often struggle under data sparsity and noise. To address these issues, we implemented LightGCL, a graph contrastive learning model that uses Singular Value Decomposition (SVD) for robust graph augmentation, preserving semantic integrity without relying on stochastic or heuristic perturbations. LightGCL enables structural refinement and captures global collaborative signals, achieving significant gains over state-of-the-art models across benchmark datasets. Our experiments also demonstrate improved fairness and resilience to popularity bias, making it well-suited for real-world recommender systems. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_00048 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Graph Contrastive Learning for Optimizing Sparse Data in Recommender Systems with LightGCL Jatavallabha, Aravinda Bharadwaj, Prabhanjan Chander, Ashish Information Retrieval Machine Learning Graph Neural Networks (GNNs) are powerful tools for recommendation systems, but they often struggle under data sparsity and noise. To address these issues, we implemented LightGCL, a graph contrastive learning model that uses Singular Value Decomposition (SVD) for robust graph augmentation, preserving semantic integrity without relying on stochastic or heuristic perturbations. LightGCL enables structural refinement and captures global collaborative signals, achieving significant gains over state-of-the-art models across benchmark datasets. Our experiments also demonstrate improved fairness and resilience to popularity bias, making it well-suited for real-world recommender systems. |
| title | Graph Contrastive Learning for Optimizing Sparse Data in Recommender Systems with LightGCL |
| topic | Information Retrieval Machine Learning |
| url | https://arxiv.org/abs/2506.00048 |