  Online communities such as Facebook and Twitter are enormously popular and
have become an essential part of the daily life of many of their users. Through
these platforms, users can discover and create information that others will
then consume. In that context, recommending relevant information to users
becomes critical for viability. However, recommendation in online communities
is a challenging problem: 1) users' interests are dynamic, and 2) users are
influenced by their friends. Moreover, the influencers may be
context-dependent. That is, different friends may be relied upon for different
topics. Modeling both signals is therefore essential for recommendations.
  We propose a recommender system for online communities based on a
dynamic-graph-attention neural network. We model dynamic user behaviors with a
recurrent neural network, and context-dependent social influence with a
graph-attention neural network, which dynamically infers the influencers based
on users' current interests. The whole model can be efficiently fit on
large-scale data. Experimental results on several real-world data sets
demonstrate the effectiveness of our proposed approach over several competitive
baselines including state-of-the-art models.
