  Recommender systems often grapple with noisy implicit feedback. Most studies
alleviate the noise issues from data cleaning perspective such as data
resampling and reweighting, but they are constrained by heuristic assumptions.
Another denoising avenue is from model perspective, which proactively injects
noises into user-item interactions and enhances the intrinsic denoising ability
of models. However, this kind of denoising process poses significant challenges
to the recommender model's representation capacity to capture noise patterns.
  To address this issue, we propose Denoising Diffusion Recommender Model
(DDRM), which leverages multi-step denoising process of diffusion models to
robustify user and item embeddings from any recommender models. DDRM injects
controlled Gaussian noises in the forward process and iteratively removes
noises in the reverse denoising process, thereby improving embedding robustness
against noisy feedback. To achieve this target, the key lies in offering
appropriate guidance to steer the reverse denoising process and providing a
proper starting point to start the forward-reverse process during inference. In
particular, we propose a dedicated denoising module that encodes collaborative
information as denoising guidance. Besides, in the inference stage, DDRM
utilizes the average embeddings of users' historically liked items as the
starting point rather than using pure noise since pure noise lacks
personalization, which increases the difficulty of the denoising process.
Extensive experiments on three datasets with three representative backend
recommender models demonstrate the effectiveness of DDRM.
