RecGPT: Generative Pre-training for Text-based Recommendation
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. The general architecture and experimental results of RecGPT can be found in our
paper
:
@inproceedings{RecGPT,
title = {{RecGPT: Generative Pre-training for Text-based Recommendation}},
author = {Hoang Ngo and Dat Quoc Nguyen},
booktitle = {Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics},
year = {2024}
}
We publicly release the RecGPT models along with their pre-training and fine-tuning datasets.
Please cite our paper whenever RecGPT or the datasets are used to help produce published results or are incorporated into other software.
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