A Temporal Graph Network Framework for Dynamic Recommendation
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866916536661311488 |
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| author | Kim, Yejin Lee, Youngbin Yuan, Vincent Lee, Annika Lee, Yongjae |
| author_facet | Kim, Yejin Lee, Youngbin Yuan, Vincent Lee, Annika Lee, Yongjae |
| contents | Recommender systems, crucial for user engagement on platforms like e-commerce and streaming services, often lag behind users' evolving preferences due to static data reliance. After Temporal Graph Networks (TGNs) were proposed, various studies have shown that TGN can significantly improve situations where the features of nodes and edges dynamically change over time. However, despite its promising capabilities, it has not been directly applied in recommender systems to date. Our study bridges this gap by directly implementing Temporal Graph Networks (TGN) in recommender systems, a first in this field. Using real-world datasets and a range of graph and history embedding methods, we show TGN's adaptability, confirming its effectiveness in dynamic recommendation scenarios. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_16066 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A Temporal Graph Network Framework for Dynamic Recommendation Kim, Yejin Lee, Youngbin Yuan, Vincent Lee, Annika Lee, Yongjae Artificial Intelligence Recommender systems, crucial for user engagement on platforms like e-commerce and streaming services, often lag behind users' evolving preferences due to static data reliance. After Temporal Graph Networks (TGNs) were proposed, various studies have shown that TGN can significantly improve situations where the features of nodes and edges dynamically change over time. However, despite its promising capabilities, it has not been directly applied in recommender systems to date. Our study bridges this gap by directly implementing Temporal Graph Networks (TGN) in recommender systems, a first in this field. Using real-world datasets and a range of graph and history embedding methods, we show TGN's adaptability, confirming its effectiveness in dynamic recommendation scenarios. |
| title | A Temporal Graph Network Framework for Dynamic Recommendation |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2403.16066 |