Graph-Sequential Alignment and Uniformity: Toward Enhanced Recommendation Systems
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arXiv
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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
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| _version_ | 1866915126654795776 |
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| author | Cao, Yuwei Yang, Liangwei Liu, Zhiwei Liu, Yuqing Wang, Chen Liang, Yueqing Peng, Hao Yu, Philip S. |
| author_facet | Cao, Yuwei Yang, Liangwei Liu, Zhiwei Liu, Yuqing Wang, Chen Liang, Yueqing Peng, Hao Yu, Philip S. |
| contents | Graph-based and sequential methods are two popular recommendation paradigms, each excelling in its domain but lacking the ability to leverage signals from the other. To address this, we propose a novel method that integrates both approaches for enhanced performance. Our framework uses Graph Neural Network (GNN)-based and sequential recommenders as separate submodules while sharing a unified embedding space optimized jointly. To enable positive knowledge transfer, we design a loss function that enforces alignment and uniformity both within and across submodules. Experiments on three real-world datasets demonstrate that the proposed method significantly outperforms using either approach alone and achieves state-of-the-art results. Our implementations are publicly available at https://github.com/YuweiCao-UIC/GSAU.git. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_04276 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Graph-Sequential Alignment and Uniformity: Toward Enhanced Recommendation Systems Cao, Yuwei Yang, Liangwei Liu, Zhiwei Liu, Yuqing Wang, Chen Liang, Yueqing Peng, Hao Yu, Philip S. Information Retrieval Graph-based and sequential methods are two popular recommendation paradigms, each excelling in its domain but lacking the ability to leverage signals from the other. To address this, we propose a novel method that integrates both approaches for enhanced performance. Our framework uses Graph Neural Network (GNN)-based and sequential recommenders as separate submodules while sharing a unified embedding space optimized jointly. To enable positive knowledge transfer, we design a loss function that enforces alignment and uniformity both within and across submodules. Experiments on three real-world datasets demonstrate that the proposed method significantly outperforms using either approach alone and achieves state-of-the-art results. Our implementations are publicly available at https://github.com/YuweiCao-UIC/GSAU.git. |
| title | Graph-Sequential Alignment and Uniformity: Toward Enhanced Recommendation Systems |
| topic | Information Retrieval |
| url | https://arxiv.org/abs/2412.04276 |