RecGPT: Generative Pre-training for Text-based Recommendation

Fuente: arXiv
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Autores principales: Ngo, Hoang, Nguyen, Dat Quoc
Formato: Preprint
Publicado: 2024
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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