  Sequential recommendation (SRS) has become the technical foundation in many
applications recently, which aims to recommend the next item based on the
user's historical interactions. However, sequential recommendation often faces
the problem of data sparsity, which widely exists in recommender systems.
Besides, most users only interact with a few items, but existing SRS models
often underperform these users. Such a problem, named the long-tail user
problem, is still to be resolved. Data augmentation is a distinct way to
alleviate these two problems, but they often need fabricated training
strategies or are hindered by poor-quality generated interactions. To address
these problems, we propose a Diffusion Augmentation for Sequential
Recommendation (DiffuASR) for a higher quality generation. The augmented
dataset by DiffuASR can be used to train the sequential recommendation models
directly, free from complex training procedures. To make the best of the
generation ability of the diffusion model, we first propose a diffusion-based
pseudo sequence generation framework to fill the gap between image and sequence
generation. Then, a sequential U-Net is designed to adapt the diffusion noise
prediction model U-Net to the discrete sequence generation task. At last, we
develop two guide strategies to assimilate the preference between generated and
origin sequences. To validate the proposed DiffuASR, we conduct extensive
experiments on three real-world datasets with three sequential recommendation
models. The experimental results illustrate the effectiveness of DiffuASR. As
far as we know, DiffuASR is one pioneer that introduce the diffusion model to
the recommendation.
