ContextGNN: Beyond Two-Tower 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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| _version_ | 1866915040094846976 |
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| author | Yuan, Yiwen Zhang, Zecheng He, Xinwei Nitta, Akihiro Hu, Weihua Wang, Dong Shah, Manan Huang, Shenyang Stojanovič, Blaž Krumholz, Alan Lenssen, Jan Eric Leskovec, Jure Fey, Matthias |
| author_facet | Yuan, Yiwen Zhang, Zecheng He, Xinwei Nitta, Akihiro Hu, Weihua Wang, Dong Shah, Manan Huang, Shenyang Stojanovič, Blaž Krumholz, Alan Lenssen, Jan Eric Leskovec, Jure Fey, Matthias |
| contents | Recommendation systems predominantly utilize two-tower architectures, which evaluate user-item rankings through the inner product of their respective embeddings. However, one key limitation of two-tower models is that they learn a pair-agnostic representation of users and items. In contrast, pair-wise representations either scale poorly due to their quadratic complexity or are too restrictive on the candidate pairs to rank. To address these issues, we introduce Context-based Graph Neural Networks (ContextGNNs), a novel deep learning architecture for link prediction in recommendation systems. The method employs a pair-wise representation technique for familiar items situated within a user's local subgraph, while leveraging two-tower representations to facilitate the recommendation of exploratory items. A final network then predicts how to fuse both pair-wise and two-tower recommendations into a single ranking of items. We demonstrate that ContextGNN is able to adapt to different data characteristics and outperforms existing methods, both traditional and GNN-based, on a diverse set of practical recommendation tasks, improving performance by 20% on average. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_19513 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | ContextGNN: Beyond Two-Tower Recommendation Systems Yuan, Yiwen Zhang, Zecheng He, Xinwei Nitta, Akihiro Hu, Weihua Wang, Dong Shah, Manan Huang, Shenyang Stojanovič, Blaž Krumholz, Alan Lenssen, Jan Eric Leskovec, Jure Fey, Matthias Information Retrieval Machine Learning Recommendation systems predominantly utilize two-tower architectures, which evaluate user-item rankings through the inner product of their respective embeddings. However, one key limitation of two-tower models is that they learn a pair-agnostic representation of users and items. In contrast, pair-wise representations either scale poorly due to their quadratic complexity or are too restrictive on the candidate pairs to rank. To address these issues, we introduce Context-based Graph Neural Networks (ContextGNNs), a novel deep learning architecture for link prediction in recommendation systems. The method employs a pair-wise representation technique for familiar items situated within a user's local subgraph, while leveraging two-tower representations to facilitate the recommendation of exploratory items. A final network then predicts how to fuse both pair-wise and two-tower recommendations into a single ranking of items. We demonstrate that ContextGNN is able to adapt to different data characteristics and outperforms existing methods, both traditional and GNN-based, on a diverse set of practical recommendation tasks, improving performance by 20% on average. |
| title | ContextGNN: Beyond Two-Tower Recommendation Systems |
| topic | Information Retrieval Machine Learning |
| url | https://arxiv.org/abs/2411.19513 |