  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.
