Graph and Sequential Neural Networks in Session-based Recommendation: A Survey
<|reference_start|>Graph and Sequential Neural Networks in Session-based Recommendation: A Survey: 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.<|reference_end|>
@article{li2024graph,
    title={Graph and Sequential Neural Networks in Session-based Recommendation: A
  Survey},
    author={Zihao Li, Chao Yang, Yakun Chen, Xianzhi Wang, Hongxu Chen, Guandong
  Xu, Lina Yao, Quan Z. Sheng},
    journal={arXiv preprint arXiv:2408.14851},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.14851},
    primaryClass={cs.IR}
}