  As important side information, attributes have been widely exploited in the
existing recommender system for better performance. In the real-world
scenarios, it is common that some attributes of items/users are missing (e.g.,
some movies miss the genre data). Prior studies usually use a default value
(i.e., "other") to represent the missing attribute, resulting in sub-optimal
performance. To address this problem, in this paper, we present an
attribute-aware attentive graph convolution network (A${^2}$-GCN). In
particular, we first construct a graph, whereby users, items, and attributes
are three types of nodes and their associations are edges. Thereafter, we
leverage the graph convolution network to characterize the complicated
interactions among <users, items, attributes>. To learn the node
representation, we turn to the message-passing strategy to aggregate the
message passed from the other directly linked types of nodes (e.g., a user or
an attribute). To this end, we are capable of incorporating associate
attributes to strengthen the user and item representations, and thus naturally
solve the attribute missing problem. Considering the fact that for different
users, the attributes of an item have different influence on their preference
for this item, we design a novel attention mechanism to filter the message
passed from an item to a target user by considering the attribute information.
Extensive experiments have been conducted on several publicly accessible
datasets to justify our model. Results show that our model outperforms several
state-of-the-art methods and demonstrate the effectiveness of our attention
method.
