  Different from the traditional recommender system, the session-based
recommender system introduces the concept of the session, i.e., a sequence of
interactions between a user and multiple items within a period, to preserve the
user's recent interest. The existing work on the session-based recommender
system mainly relies on mining sequential patterns within individual sessions,
which are not expressive enough to capture more complicated dependency
relationships among items. In addition, it does not consider the cross-session
information due to the anonymity of the session data, where the linkage between
different sessions is prevented. In this paper, we solve these problems with
the graph neural networks technique. First, each session is represented as a
graph rather than a linear sequence structure, based on which a novel Full
Graph Neural Network (FGNN) is proposed to learn complicated item dependency.
To exploit and incorporate cross-session information in the individual
session's representation learning, we further construct a Broadly Connected
Session (BCS) graph to link different sessions and a novel Mask-Readout
function to improve session embedding based on the BCS graph. Extensive
experiments have been conducted on two e-commerce benchmark datasets, i.e.,
Yoochoose and Diginetica, and the experimental results demonstrate the
superiority of our proposal through comparisons with state-of-the-art
session-based recommender models.
