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.
@article{arxiv.2306.08947,
title = {RecFusion: A Binomial Diffusion Process for 1D Data for Recommendation},
author = {Gabriel Bénédict and Olivier Jeunen and Samuele Papa and Samarth Bhargav and Daan Odijk and Maarten de Rijke},
journal= {arXiv preprint arXiv:2306.08947},
year = {2023}
}