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:
this https URL

我们提出了第一个经过领域适应和完全训练的大型语言模型 RecGPT-7B 及其指导模式 RecGPT-7B-Instruct，用于基于文本的推荐。实验结果表明，我们的模型 RecGPT-7B-Instruct 在评分预测和顺序推荐任务上优于以前的强基准模型。我们释放了 RecGPT 模型以及它们的预训练和微调数据集，以促进未来在基于文本的推荐领域的研究和应用。可以在此链接获取我们 RecGPT 模型和数据集：https://huggingface.com/

RecGPT：文本推荐的生成式预训练

RecGPT: Generative Pre-training for Text-based Recommendation

Prior study has shown that pretrained language models (PLM) can boost the
performance of text-based recommendation. In contrast to previous works that
either use PLM to encode user history as a whole input text, or impose an
additional aggregation network to fuse multi-turn history representations, we
propose a unified local- and global-attention Transformer encoder to better
model two-level contexts of user history. Moreover, conditioned on user history
encoded by Transformer encoders, our framework leverages Transformer decoders
to estimate the language perplexity of candidate text items, which can serve as
a straightforward yet significant contrastive signal for user-item text
matching. Based on this, our framework, UniTRec, unifies the contrastive
objectives of discriminative matching scores and candidate text perplexity to
jointly enhance text-based recommendation. Extensive evaluation shows that
UniTRec delivers SOTA performance on three text-based recommendation tasks.
Code is available at this https URL

本文提出了一种名为 UniTRec 的框架，它使用预训练语言模型来增强基于文本的推荐系统，其中该框架使用 Transformer 编码器和解码器来处理用户历史和候选文本，利用语言困惑度作为对比信号进行匹配，展现了 SOTA 的表现。