Graph and Sequential Neural Networks in Session-based Recommendation: A Survey
Zihao Li, Chao Yang, Yakun Chen, Xianzhi Wang, Hongxu Chen, Guandong Xu, Lina Yao, Quan Z. Sheng
Abstract
Recent years have witnessed the remarkable success of recommendation systems (RSs) in alleviating the information overload problem. As a new paradigm of RSs, session-based recommendation (SR) specializes in users’ short-term preferences and aims at providing a more dynamic and timely recommendation based on ongoing interactions. This survey presents a comprehensive overview of the recent works on SR. First, we clarify the key definitions within SR and compare the characteristics of SR against other recommendation tasks. Then, we summarize the existing methods in two categories: sequential neural network based methods and graph neural network (GNN) based methods. The relevant frameworks and technical details are further introduced. Finally, we discuss the challenges of SR and new research directions in this area.