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

arXiv: https://arxiv.org/abs/2408.14851 · HTML: https://mlsanity.com/paper/2408.14851/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Aug 2024.

## Built on by (most cited first, 7 of 7)

- [FinTRec: Transformer Based Unified Contextual Ads Targeting and Personalization for Financial Applications](https://mlsanity.com/paper/2511.14865/) (2025)
- [Listwise Preference Alignment Optimization for Tail Item Recommendation](https://mlsanity.com/paper/2507.02255/) (2025)
- [Integrating Sequential and Relational Modeling for User Events: Datasets and Prediction Tasks](https://mlsanity.com/paper/2510.11903/) (2025)
- [Escaping the Euclidean Void: Manifold-Informed Flow Matching for Sequential Recommendation](https://mlsanity.com/paper/2607.23762/) (2026)
- [Compositions of Variant Experts for Integrating Short-Term and Long-Term Preferences](https://mlsanity.com/paper/2506.23170/) (2025)
- [Bid Farewell to Seesaw: Towards Accurate Long-tail Session-based Recommendation via Dual Constraints of Hybrid Intents](https://mlsanity.com/paper/2511.08378/) (2025)
- [Sequential Learner Modeling Using Multi-Relational Graph Convolutional Networks](https://mlsanity.com/paper/2607.19253/) (2026)

## Abstract (arXiv)

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.