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

2024/08/27 by Zihao Li, Li, Zihao, Chao Yang +13 · 4 citations
Computer Science · #FOS: Computer and information sciences #Information Retrieval (cs.IR) #Machine Learning in Healthcare #Recommender Systems and Techniques #Topic Modeling

paper · pdf · doi:10.48550/arxiv.2408.14851

openalex publication_date 2024/08/27 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

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.

Cited by

• Escaping the Euclidean Void: Manifold-Informed Flow Matching for Sequential Recommendation
• FinTRec: Transformer Based Unified Contextual Ads Targeting and Personalization for Financial Applications
• Continuous Online Evaluation of Recommendation Strategies in Social Science Academic Search
• Integrating Sequential and Relational Modeling for User Events: Datasets and Prediction Tasks

Related

• SR-HetGNN:Session-based Recommendation with Heterogeneous Graph Neural Network
• A GNN Model with Adaptive Weights for Session-Based Recommendation Systems
• Heterogeneous Graph Neural Network for Personalized Session-Based Recommendation with User-Session Constraints
• Session-based Recommendations with Recurrent Neural Networks
• Intention Adaptive Graph Neural Network for Category-aware Session-based Recommendation