  A recent study has shown that diffusion models are well-suited for modeling
the generative process of user-item interactions in recommender systems due to
their denoising nature. However, existing diffusion model-based recommender
systems do not explicitly leverage high-order connectivities that contain
crucial collaborative signals for accurate recommendations. Addressing this
gap, we propose CF-Diff, a new diffusion model-based collaborative filtering
(CF) method, which is capable of making full use of collaborative signals along
with multi-hop neighbors. Specifically, the forward-diffusion process adds
random noise to user-item interactions, while the reverse-denoising process
accommodates our own learning model, named cross-attention-guided multi-hop
autoencoder (CAM-AE), to gradually recover the original user-item interactions.
CAM-AE consists of two core modules: 1) the attention-aided AE module,
responsible for precisely learning latent representations of user-item
interactions while preserving the model's complexity at manageable levels, and
2) the multi-hop cross-attention module, which judiciously harnesses high-order
connectivity information to capture enhanced collaborative signals. Through
comprehensive experiments on three real-world datasets, we demonstrate that
CF-Diff is (a) Superior: outperforming benchmark recommendation methods,
achieving remarkable gains up to 7.29% compared to the best competitor, (b)
Theoretically-validated: reducing computations while ensuring that the
embeddings generated by our model closely approximate those from the original
cross-attention, and (c) Scalable: proving the computational efficiency that
scales linearly with the number of users or items.
