Session-based Recommendation with Graph Neural Networks

Understand

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.