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.

翻译：本文提出RecFusion，这是一组专为推荐任务设计的扩散模型。与包含空间相关性的图像数据不同，推荐系统中常用的用户-物品交互矩阵缺乏用户与物品之间的空间关系。我们将扩散过程定义在一维向量上，并提出二项扩散方法，通过伯努利过程显式建模二进制的用户-物品交互。实验表明，在核心推荐设置（面向二进制非序列反馈的top-n推荐）及最常用数据集（MovieLens和Netflix）上，RecFusion的性能可与复杂VAE基线模型相媲美。我们提出的专用于一维和/或二进制场景的扩散模型，其应用潜力不仅限于推荐系统，还可拓展至医疗领域（如MRI和CT扫描）。