  In many recommender systems, users and items are associated with attributes,
and users show preferences to items. The attribute information describes
users'(items') characteristics and has a wide range of applications, such as
user profiling, item annotation, and feature-enhanced recommendation. As
annotating user (item) attributes is a labor intensive task, the attribute
values are often incomplete with many missing attribute values. Therefore, item
recommendation and attribute inference have become two main tasks in these
platforms. Researchers have long converged that user (item) attributes and the
preference behavior are highly correlated. Some researchers proposed to
leverage one kind of data for the remaining task, and showed to improve
performance. Nevertheless, these models either neglected the incompleteness of
user (item) attributes or regarded the correlation of the two tasks with simple
models, leading to suboptimal performance of these two tasks. To this end, in
this paper, we define these two tasks in an attributed user-item bipartite
graph, and propose an Adaptive Graph Convolutional Network (AGCN) approach for
joint item recommendation and attribute inference. The key idea of AGCN is to
iteratively perform two parts: 1) Learning graph embedding parameters with
previously learned approximated attribute values to facilitate two tasks; 2)
Sending the approximated updated attribute values back to the attributed graph
for better graph embedding learning. Therefore, AGCN could adaptively adjust
the graph embedding learning parameters by incorporating both the given
attributes and the estimated attribute values, in order to provide weakly
supervised information to refine the two tasks. Extensive experimental results
on three real-world datasets clearly show the effectiveness of the proposed
model.
