Session-based recommendation tries to make use of anonymous session data to
deliver high-quality recommendation under the condition that user-profiles and
the complete historical behavioral data of a target user are unavailable.
Previous works consider each session individually and try to capture user
interests within a session. Despite their encouraging results, these models can
only perceive intra-session items and cannot draw upon the massive historical
relational information. To solve this problem, we propose a novel method named
G$^3$SR (Global Graph Guided Session-based Recommendation). G$^3$SR decomposes
the session-based recommendation workflow into two steps. First, a global graph
is built upon all session data, from which the global item representations are
learned in an unsupervised manner. Then, these representations are refined on
session graphs under the graph networks, and a readout function is used to
generate session representations for each session. Extensive experiments on two
real-world benchmark datasets show remarkable and consistent improvements of
the G$^3$SR method over the state-of-the-art methods, especially for cold
items.

研究了基于 Session 的推荐算法，并提出一种名为 G3SR 的新方法，在两个真实世界的基准数据集上进行了广泛的实验，表明改进后的方法在冷启动情况下比现有的最先进方法取得了显著的并且一致的改进效果。