  The problem of session-based recommendation aims to predict user actions
based on anonymous sessions. Previous methods model a session as a sequence and
estimate user representations besides item representations to make
recommendations. Though achieved promising results, they are insufficient to
obtain accurate user vectors in sessions and neglect complex transitions of
items. To obtain accurate item embedding and take complex transitions of items
into account, we propose a novel method, i.e. Session-based Recommendation with
Graph Neural Networks, SR-GNN for brevity. In the proposed method, session
sequences are modeled as graph-structured data. Based on the session graph, GNN
can capture complex transitions of items, which are difficult to be revealed by
previous conventional sequential methods. Each session is then represented as
the composition of the global preference and the current interest of that
session using an attention network. Extensive experiments conducted on two real
datasets show that SR-GNN evidently outperforms the state-of-the-art
session-based recommendation methods consistently.
