Denoising Diffusion Probabilistic Model (DDPM) has shown great competence in image and audio generation tasks. However, there exist few attempts to employ DDPM in the text generation, especially review generation under recommendation systems. Fueled by the predicted reviews explainability that justifies recommendations could assist users better understand the recommended items and increase the transparency of recommendation system, we propose a Diffusion Model-based Review Generation towards EXplainable Recommendation named Diffusion-EXR. Diffusion-EXR corrupts the sequence of review embeddings by incrementally introducing varied levels of Gaussian noise to the sequence of word embeddings and learns to reconstruct the original word representations in the reverse process. The nature of DDPM enables our lightweight Transformer backbone to perform excellently in the recommendation review generation task. Extensive experimental results have demonstrated that Diffusion-EXR can achieve state-of-the-art review generation for recommendation on two publicly available benchmark datasets.

翻译：去噪扩散概率模型（DDPM）在图像和音频生成任务中已展现出卓越能力。然而，目前鲜有研究尝试将DDPM应用于文本生成领域，尤其是在推荐系统下的评论生成任务。鉴于预测评论的可解释性能够帮助用户更好地理解推荐项目并提升推荐系统的透明度，我们提出了一种基于扩散模型的可解释推荐评论生成方法——Diffusion-EXR。该方法通过在词嵌入序列中逐步引入不同强度的高斯噪声来破坏评论嵌入序列，并在逆向过程中学习重建原始词表征。DDPM的本质特性使得我们轻量化的Transformer主干网络能够在推荐评论生成任务中表现出色。大量实验结果表明，Diffusion-EXR在两个公开基准数据集上能够实现最先进的推荐评论生成效果。