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展现出卓越性能。然而，目前鲜有研究将DDPM应用于文本生成领域，特别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理解推荐项目并增强推荐系统的透明度，我们提出一种基于扩散模型的解释性推荐评论生成框架Diffusion-EXR。该方法通过对词嵌入序列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示。DDPM的本质特性使得我们采用轻量级Transformer骨干网络即可在推荐评论生成任务中取得优异表现。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当前最先进的推荐评论生成效果。