  The remarkable progress of network embedding has led to state-of-the-art
algorithms in recommendation. However, the sparsity of user-item interactions
(i.e., explicit preferences) on websites remains a big challenge for predicting
users' behaviors. Although research efforts have been made in utilizing some
auxiliary information (e.g., social relations between users) to solve the
problem, the existing rich heterogeneous auxiliary relationships are still not
fully exploited. Moreover, previous works relied on linearly combined
regularizers and suffered parameter tuning.
  In this work, we collect abundant relationships from common user behaviors
and item information, and propose a novel framework named IntentGC to leverage
both explicit preferences and heterogeneous relationships by graph
convolutional networks. In addition to the capability of modeling
heterogeneity, IntentGC can learn the importance of different relationships
automatically by the neural model in a nonlinear sense. To apply IntentGC to
web-scale applications, we design a faster graph convolutional model named
IntentNet by avoiding unnecessary feature interactions. Empirical experiments
on two large-scale real-world datasets and online A/B tests in Alibaba
demonstrate the superiority of our method over state-of-the-art algorithms.
