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.

本研究提出了一种新颖的目标关注图神经网络（TAGNN）模型，以用于会话推荐。TAGNN 通过考虑用户的不同兴趣点并学习每个目标项的相关性来提高表达能力。此外，TAGNN 利用图神经网络的能力来捕获会话中的丰富过渡，通过真实世界数据集的广泛实验，证明其优于现有方法。