  The problem of session-aware recommendation aims to predict users' next click
based on their current session and historical sessions. Existing session-aware
recommendation methods have defects in capturing complex item transition
relationships. Other than that, most of them fail to explicitly distinguish the
effects of different historical sessions on the current session. To this end,
we propose a novel method, named Personalized Graph Neural Networks with
Attention Mechanism (A-PGNN) for brevity. A-PGNN mainly consists of two
components: one is Personalized Graph Neural Network (PGNN), which is used to
extract the personalized structural information in each user behavior graph,
compared with the traditional Graph Neural Network (GNN) model, which considers
the role of the user when the node embeddding is updated. The other is
Dot-Product Attention mechanism, which draws on the Transformer net to
explicitly model the effect of historical sessions on the current session.
Extensive experiments conducted on two real-world data sets show that A-PGNN
evidently outperforms the state-of-the-art personalized session-aware
recommendation methods.
