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.

通过提出 CF-Diff 方法，结合扩展的协作标志、多跳邻居以及交叉注意力引导的多跳自动编码器（CAM-AE），克服了现有扩散模型推荐系统在准确推荐方面无法明确利用包含关键协作信号的高阶连接问题。实验证明，CF-Diff 在三个真实世界数据集上优于基准推荐方法，相对最佳竞争对手的表现可达到 7.29％的显著增益，并确保了计算效率与用户或物品数量呈线性比例。