  Social recommendation has emerged to leverage social connections among users
for predicting users' unknown preferences, which could alleviate the data
sparsity issue in collaborative filtering based recommendation. Early
approaches relied on utilizing each user's first-order social neighbors'
interests for better user modeling and failed to model the social influence
diffusion process from the global social network structure. Recently, we
propose a preliminary work of a neural influence diffusion network (i.e.,
DiffNet) for social recommendation (Diffnet), which models the recursive social
diffusion process to capture the higher-order relationships for each user.
However, 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 in the social network would neglect the users' latent collaborative
interests in the user-item interest network. In this paper, we propose
DiffNet++, an improved algorithm of DiffNet that models the neural influence
diffusion and interest diffusion in a unified framework. By reformulating the
social recommendation as a heterogeneous graph with social network and interest
network as input, DiffNet++ advances DiffNet by injecting these two network
information for user embedding learning at the same time. This is achieved by
iteratively aggregating each user's embedding from three aspects: the user's
previous embedding, the influence aggregation of social neighbors from the
social network, and the interest aggregation of item neighbors from the
user-item interest network. Furthermore, we design a multi-level attention
network that learns how to attentively aggregate user embeddings from these
three aspects. Finally, extensive experimental results on two real-world
datasets clearly show the effectiveness of our proposed model.
