In this paper we propose RecFusion, which comprise a set of diffusion models
for recommendation. Unlike image data which contain spatial correlations, a
user-item interaction matrix, commonly utilized in recommendation, lacks
spatial relationships between users and items. We formulate diffusion on a 1D
vector and propose binomial diffusion, which explicitly models binary user-item
interactions with a Bernoulli process. We show that RecFusion approaches the
performance of complex VAE baselines on the core recommendation setting (top-n
recommendation for binary non-sequential feedback) and the most common datasets
(MovieLens and Netflix). Our proposed diffusion models that are specialized for
1D and/or binary setups have implications beyond recommendation systems, such
as in the medical domain with MRI and CT scans.

本文提出了 RecFusion，它包含一组扩散模型，用于推荐。我们在一个一维向量上制定了扩散并提出了二项式扩散，它用伯努利过程明确地建模了二元用户 - 项目交互。我们展示了 RecFusion 在核心推荐设置（二元非顺序反馈的 top-n 推荐）和最常见的数据集（MovieLens 和 Netflix）上接近于复杂 VAE 基线的性能。我们所提出的针对 1D 和 / 或二元设置的扩散模型具有医学领域 MRI 和 CT 扫描等超越推荐系统的含义。