  Social recommendation leverages social information to solve data sparsity and
cold-start problems in traditional collaborative filtering methods. However,
most existing models assume that social effects from friend users are static
and under the forms of constant weights or fixed constraints. To relax this
strong assumption, in this paper, we propose dual graph attention networks to
collaboratively learn representations for two-fold social effects, where one is
modeled by a user-specific attention weight and the other is modeled by a
dynamic and context-aware attention weight. We also extend the social effects
in user domain to item domain, so that information from related items can be
leveraged to further alleviate the data sparsity problem. Furthermore,
considering that different social effects in two domains could interact with
each other and jointly influence user preferences for items, we propose a new
policy-based fusion strategy based on contextual multi-armed bandit to weigh
interactions of various social effects. Experiments on one benchmark dataset
and a commercial dataset verify the efficacy of the key components in our
model. The results show that our model achieves great improvement for
recommendation accuracy compared with other state-of-the-art social
recommendation methods.
