RecFusion: A Binomial Diffusion Process for 1D Data for Recommendation

• URL: http://arxiv.org/abs/2306.08947v3
• Date: Thu, 7 Sep 2023 20:55:29 GMT
• Title: RecFusion: A Binomial Diffusion Process for 1D Data for Recommendation
• Authors: Gabriel B\'en\'edict, Olivier Jeunen, Samuele Papa, Samarth Bhargav, Daan Odijk, Maarten de Rijke
• Abstract summary: We propose RecFusion, which comprises a set of diffusion models for recommendation. We formulate diffusion on a 1D vector and propose binomial diffusion, which explicitly models binary user-item interactions with a Bernoulli process. Our proposed diffusion models have implications beyond recommendation systems, such as in the medical domain with MRI and CT scans.
• Score: 48.77168472848952
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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