  Multimedia recommendation aims to predict users' future behaviors based on
historical behavioral data and item's multimodal information. However, noise
inherent in behavioral data, arising from unintended user interactions with
uninteresting items, detrimentally impacts recommendation performance.
Recently, diffusion models have achieved high-quality information generation,
in which the reverse process iteratively infers future information based on the
corrupted state. It meets the need of predictive tasks under noisy conditions,
and inspires exploring their application to predicting user behaviors.
Nonetheless, several challenges must be addressed: 1) Classical diffusion
models require excessive computation, which does not meet the efficiency
requirements of recommendation systems. 2) Existing reverse processes are
mainly designed for continuous data, whereas behavioral information is discrete
in nature. Therefore, an effective method is needed for the generation of
discrete behavioral information.
  To tackle the aforementioned issues, we propose a Light Diffusion model for
Multimedia Recommendation. First, to reduce computational complexity, we
simplify the formula of the reverse process, enabling one-step inference
instead of multi-step inference. Second, to achieve effective behavioral
information generation, we propose a novel Conditional neural Network. It maps
the discrete behavior data into a continuous latent space, and generates
behaviors with the guidance of collaborative signals and user multimodal
preference. Additionally, considering that completely clean behavior data is
inaccessible, we introduce a soft behavioral reconstruction constraint during
model training, facilitating behavior prediction with noisy data. Empirical
studies conducted on three public datasets demonstrate the effectiveness of
LD4MRec.
