DiffNet++: A Neural Influence and Interest Diffusion Network for Social Recommendation

Social recommendation has emerged to leverage social connections among users for recommendation Early approaches relied on utilizing each user's first-order social neighbors' interests for better user modeling. Recently, we propose a preliminary work of a neural influence Diffusion Network (i.e., DiffNet) to model the recursive social diffusion process for each user, such that the influence diffusion hidden in the higher-order social network is captured. Despite the superior performance of DiffNet, we argue that, as users play a central role in both user-user social network and user-item interest network, only modeling the influence diffusion process would neglect the latent collaborative interests of users hidden in the user-item interest network. To this end, we propose DiffNet++, an improved algorithm of DiffNet that models the neural influence diffusion and interest diffusion in a unified framework. Specifically, DiffNet++ advances DiffNet by injecting both the higher-order user latent interest reflected in the user-item graph and higher-order user influence reflected in the user-user graph for user embedding learning. Furthermore, we design a multi-level attention network that learns how to attentively aggregate user embeddings from different graphs. Finally, extensive experimental results on two real-world datasets clearly show the effectiveness of our proposed model.