Graph and Sequential Neural Networks in Session-based Recommendation: A Survey

TL;DR

This survey reviews recent advances in session-based recommendation systems, focusing on neural network and graph neural network methods, highlighting their frameworks, challenges, and future research directions.

Contribution

It provides a comprehensive overview of SR tasks, categorizes existing methods, and discusses technical frameworks and future challenges in the field.

Findings

Sequential neural network methods effectively model user sessions.

Graph neural network approaches capture complex session relationships.

Identifies key challenges and promising directions for SR research.

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 preference capture and aims to provide a more dynamic and timely recommendation based on the ongoing interacted actions. In this survey, we will give a comprehensive overview of the recent works on SR. First, we clarify the definitions of various SR tasks and introduce the characteristics of session-based recommendation 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 standard frameworks and technical are also introduced. Finally, we discuss the challenges of SR and new research directions in this area.