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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2411.15186 |
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| _version_ | 1866908407529734144 |
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| author | Zhan, Tianyu Lv, Zheqi Zhang, Shengyu Li, Jiwei |
| author_facet | Zhan, Tianyu Lv, Zheqi Zhang, Shengyu Li, Jiwei |
| contents | This paper explores the application and effectiveness of Test-Time Training (TTT) layers in improving the performance of recommendation systems. We developed a model, TTT4Rec, utilizing TTT-Linear as the feature extraction layer. Our tests across multiple datasets indicate that TTT4Rec, as a base model, performs comparably or even surpasses other baseline models in similar environments. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_15186 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Preliminary Evaluation of the Test-Time Training Layers in Recommendation System (Student Abstract) Zhan, Tianyu Lv, Zheqi Zhang, Shengyu Li, Jiwei Information Retrieval This paper explores the application and effectiveness of Test-Time Training (TTT) layers in improving the performance of recommendation systems. We developed a model, TTT4Rec, utilizing TTT-Linear as the feature extraction layer. Our tests across multiple datasets indicate that TTT4Rec, as a base model, performs comparably or even surpasses other baseline models in similar environments. |
| title | Preliminary Evaluation of the Test-Time Training Layers in Recommendation System (Student Abstract) |
| topic | Information Retrieval |
| url | https://arxiv.org/abs/2411.15186 |