Flow Matching for Denoised Social Recommendation

Graph-based social recommendation (SR) models suffer from various noises in social graphs, hindering their recommendation performances. Both graph-level redundancy and graph-level missing will indeed influence the social graph structures, further influencing the message propagation procedure of graph neural networks (GNNs). Generative models, especially diffusion models, are usually used to reconstruct and recover the data in better quality from noisy input. Motivated by it, a few works take attempts on it for social recommendation. However, they can only handle isotropic Gaussian noise and fail to address anisotropic noise. Moreover, an anisotropic relational structures in social graphs are commonly seen, which existing models cannot sufficiently utilize the graph structures, which constraints the capacity of noise removal and recommendation performances. Compared to the diffusion strategy, the flow matching strategy better handles anisotropic noise, as it preserves data structures more effectively during the learning process. Inspired by this, we propose RecFlow, the first flowbased SR model. Concretely, RecFlow performs flow-based method on the structure representations of social graphs. Then, a conditional learning procedure is designed for optimization. Extensive performances prove the promising performances of our RecFlow from six aspects, including superiority, effectiveness, robustnesses, sensitivity, convergence and visualization. Code are available at .