RecGPT: Generative Pre-training for Text-based Recommendation
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arXiv
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| Autores principales: | , |
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| Formato: | Preprint |
| Publicado: |
2024
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| _version_ | 1866914803963920384 |
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| author | Ngo, Hoang Nguyen, Dat Quoc |
| author_facet | Ngo, Hoang Nguyen, Dat Quoc |
| contents | We present the first domain-adapted and fully-trained large language model, RecGPT-7B, and its instruction-following variant, RecGPT-7B-Instruct, for text-based recommendation. Experimental results on rating prediction and sequential recommendation tasks show that our model, RecGPT-7B-Instruct, outperforms previous strong baselines. We are releasing our RecGPT models as well as their pre-training and fine-tuning datasets to facilitate future research and downstream applications in text-based recommendation. Public "huggingface" links to our RecGPT models and datasets are available at: https://github.com/VinAIResearch/RecGPT |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_12715 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | RecGPT: Generative Pre-training for Text-based Recommendation Ngo, Hoang Nguyen, Dat Quoc Information Retrieval Computation and Language We present the first domain-adapted and fully-trained large language model, RecGPT-7B, and its instruction-following variant, RecGPT-7B-Instruct, for text-based recommendation. Experimental results on rating prediction and sequential recommendation tasks show that our model, RecGPT-7B-Instruct, outperforms previous strong baselines. We are releasing our RecGPT models as well as their pre-training and fine-tuning datasets to facilitate future research and downstream applications in text-based recommendation. Public "huggingface" links to our RecGPT models and datasets are available at: https://github.com/VinAIResearch/RecGPT |
| title | RecGPT: Generative Pre-training for Text-based Recommendation |
| topic | Information Retrieval Computation and Language |
| url | https://arxiv.org/abs/2405.12715 |