59 research outputs found
Diffusion Recommender Model
Generative models such as Generative Adversarial Networks (GANs) and
Variational Auto-Encoders (VAEs) are widely utilized to model the generative
process of user interactions. However, these generative models suffer from
intrinsic limitations such as the instability of GANs and the restricted
representation ability of VAEs. Such limitations hinder the accurate modeling
of the complex user interaction generation procedure, such as noisy
interactions caused by various interference factors. In light of the impressive
advantages of Diffusion Models (DMs) over traditional generative models in
image synthesis, we propose a novel Diffusion Recommender Model (named DiffRec)
to learn the generative process in a denoising manner. To retain personalized
information in user interactions, DiffRec reduces the added noises and avoids
corrupting users' interactions into pure noises like in image synthesis. In
addition, we extend traditional DMs to tackle the unique challenges in
practical recommender systems: high resource costs for large-scale item
prediction and temporal shifts of user preference. To this end, we propose two
extensions of DiffRec: L-DiffRec clusters items for dimension compression and
conducts the diffusion processes in the latent space; and T-DiffRec reweights
user interactions based on the interaction timestamps to encode temporal
information. We conduct extensive experiments on three datasets under multiple
settings (e.g. clean training, noisy training, and temporal training). The
empirical results and in-depth analysis validate the superiority of DiffRec
with two extensions over competitive baselines.Comment: 11 pages, 7 figures, accepted for publication in SIGIR'2
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