Graph and Sequential Neural Networks in Session-based Recommendation: A Survey
원문 초록
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.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
Contrastive Matrix Completion with Denoising and Augmented Graph Views for Robust Recommendation
DualGNN: Dual Graph Neural Network for Multimedia Recommendation
A Deep Graph Neural Network-Based Mechanism for Social Recommendations
When Recurrent Neural Networks meet the Neighborhood for Session-Based Recommendation
Long-Short Distance Graph Neural Networks and Improved Curriculum Learning for Emotion Recognition in Conversation
A Reproducibility Study of Product-side Fairness in Bundle Recommendation
토론
이 논문과 연결된 포럼 글이 아직 없습니다.