  Session-based recommendation nowadays plays a vital role in many websites,
which aims to predict users' actions based on anonymous sessions. There have
emerged many studies that model a session as a sequence or a graph via
investigating temporal transitions of items in a session. However, these
methods compress a session into one fixed representation vector without
considering the target items to be predicted. The fixed vector will restrict
the representation ability of the recommender model, considering the diversity
of target items and users' interests. In this paper, we propose a novel target
attentive graph neural network (TAGNN) model for session-based recommendation.
In TAGNN, target-aware attention adaptively activates different user interests
with respect to varied target items. The learned interest representation vector
varies with different target items, greatly improving the expressiveness of the
model. Moreover, TAGNN harnesses the power of graph neural networks to capture
rich item transitions in sessions. Comprehensive experiments conducted on
real-world datasets demonstrate its superiority over state-of-the-art methods.
