# Sequential/Session-based Recommendations: Challenges, Approaches, Applications and Opportunities

arXiv: https://arxiv.org/abs/2205.10759 · HTML: https://mlsanity.com/paper/2205.10759/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 20 references that are in the map.
- First posted on arXiv 22 May 2022.

## Builds on (most cited first, 9 of 9)

- [Session-based Recommendation with Graph Neural Networks](https://mlsanity.com/paper/1811.00855/) (2018)
- [Contrastive Learning for Representation Degeneration Problem in Sequential Recommendation](https://mlsanity.com/paper/2110.05730/) (2021)
- [Sequential Recommender Systems: Challenges, Progress and Prospects](https://mlsanity.com/paper/2001.04830/) (2019)
- [A Survey on Session-based Recommender Systems](https://mlsanity.com/paper/1902.04864/) (2019)
- [Graph Learning based Recommender Systems: A Review](https://mlsanity.com/paper/2105.06339/) (2021)
- [Sequence-Aware Recommender Systems](https://mlsanity.com/paper/1802.08452/) (2018)
- [Deep Reinforcement Learning for List-wise Recommendations](https://mlsanity.com/paper/1801.00209/) (2017)
- [Empirical Analysis of Session-Based Recommendation Algorithms](https://mlsanity.com/paper/1910.12781/) (2019)
- [Deep Learning for Sequential Recommendation: Algorithms, Influential Factors, and Evaluations](https://mlsanity.com/paper/1905.01997/) (2019)

## Built on by (most cited first, 9 of 9)

- [TD3: Tucker Decomposition Based Dataset Distillation Method for Sequential Recommendation](https://mlsanity.com/paper/2502.02854/) (2025)
- [Enhancing User Intent Capture in Session-Based Recommendation with Attribute Patterns](https://mlsanity.com/paper/2312.16199/) (2023)
- [APGL4SR: A Generic Framework with Adaptive and Personalized Global Collaborative Information in Sequential Recommendation](https://mlsanity.com/paper/2311.02816/) (2023)
- [Dual Contrastive Transformer for Hierarchical Preference Modeling in Sequential Recommendation](https://mlsanity.com/paper/2410.22790/) (2024)
- [Towards Differential Privacy in Sequential Recommendation: A Noisy Graph Neural Network Approach](https://mlsanity.com/paper/2309.11515/) (2023)
- [Uncertainty-Aware Relational Graph Neural Network for Few-Shot Knowledge Graph Completion](https://mlsanity.com/paper/2403.04521/) (2024)
- [SOLARIS: Speculative Offloading of Latent-bAsed Representation for Inference Scaling](https://mlsanity.com/paper/2604.12110/) (2026)
- [CUPID: A Real-Time Session-Based Reciprocal Recommendation System for a One-on-One Social Discovery Platform](https://mlsanity.com/paper/2410.18087/) (2024)
- [Generative Data Transformation: From Mixed to Unified Data](https://mlsanity.com/paper/2602.22743/) (2026)

## Abstract (arXiv)

In recent years, sequential recommender systems (SRSs) and session-based recommender systems (SBRSs) have emerged as a new paradigm of RSs to capture users' short-term but dynamic preferences for enabling more timely and accurate recommendations. Although SRSs and SBRSs have been extensively studied, there are many inconsistencies in this area caused by the diverse descriptions, settings, assumptions and application domains. There is no work to provide a unified framework and problem statement to remove the commonly existing and various inconsistencies in the area of SR/SBR. There is a lack of work to provide a comprehensive and systematic demonstration of the data characteristics, key challenges, most representative and state-of-the-art approaches, typical real-world applications and important future research directions in the area. This work aims to fill in these gaps so as to facilitate further research in this exciting and vibrant area.