# A Survey on Session-based Recommender Systems

arXiv: https://arxiv.org/abs/1902.04864 · HTML: https://mlsanity.com/paper/1902.04864/

## Key facts

- Cited by 97 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Feb 2019.

## Built on by (most cited first, 30 of 97)

- [A Survey of Graph Neural Networks for Social Recommender Systems](https://mlsanity.com/paper/2212.04481/) (2022)
- [A Survey on Accuracy-oriented Neural Recommendation: From Collaborative Filtering to Information-rich Recommendation](https://mlsanity.com/paper/2104.13030/) (2021)
- [Graph Learning based Recommender Systems: A Review](https://mlsanity.com/paper/2105.06339/) (2021)
- [Amazon-M2: A Multilingual Multi-locale Shopping Session Dataset for Recommendation and Text Generation](https://mlsanity.com/paper/2307.09688/) (2023)
- [CORE: Simple and Effective Session-based Recommendation within Consistent Representation Space](https://mlsanity.com/paper/2204.11067/) (2022)
- [Heterogeneous Global Graph Neural Networks for Personalized Session-based Recommendation](https://mlsanity.com/paper/2107.03813/) (2021)
- [Trustworthy Recommender Systems](https://mlsanity.com/paper/2208.06265/) (2022)
- [Data Scarcity in Recommendation Systems: A Survey](https://mlsanity.com/paper/2312.10073/) (2023)
- [Dynamic Graph Unlearning: A General and Efficient Post-Processing Method via Gradient Transformation](https://mlsanity.com/paper/2405.14407/) (2024)
- [The Unfairness of Popularity Bias in Book Recommendation](https://mlsanity.com/paper/2202.13446/) (2022)
- [Attention Mixtures for Time-Aware Sequential Recommendation](https://mlsanity.com/paper/2304.08158/) (2023)
- [Price DOES Matter! Modeling Price and Interest Preferences in Session-based Recommendation](https://mlsanity.com/paper/2205.04181/) (2022)
- [Generating Usage-related Questions for Preference Elicitation in Conversational Recommender Systems](https://mlsanity.com/paper/2111.13463/) (2021)
- [Device-Cloud Collaborative Recommendation via Meta Controller](https://mlsanity.com/paper/2207.03066/) (2022)
- [APGL4SR: A Generic Framework with Adaptive and Personalized Global Collaborative Information in Sequential Recommendation](https://mlsanity.com/paper/2311.02816/) (2023)
- [Sequential/Session-based Recommendations: Challenges, Approaches, Applications and Opportunities](https://mlsanity.com/paper/2205.10759/) (2022)
- [RecD: Deduplication for End-to-End Deep Learning Recommendation Model Training Infrastructure](https://mlsanity.com/paper/2211.05239/) (2022)
- [Recommender systems based on graph embedding techniques: A comprehensive review](https://mlsanity.com/paper/2109.09587/) (2021)
- [Beyond NDCG: behavioral testing of recommender systems with RecList](https://mlsanity.com/paper/2111.09963/) (2021)
- [Optimizing Audio Recommendations for the Long-Term: A Reinforcement Learning Perspective](https://mlsanity.com/paper/2302.03561/) (2023)
- [PURS: Personalized Unexpected Recommender System for Improving User Satisfaction](https://mlsanity.com/paper/2106.02771/) (2021)
- [Disentangling ID and Modality Effects for Session-based Recommendation](https://mlsanity.com/paper/2404.12969/) (2024)
- [Modeling Temporal Positive and Negative Excitation for Sequential Recommendation](https://mlsanity.com/paper/2410.22013/) (2024)
- [Synthetic Data-Based Simulators for Recommender Systems: A Survey](https://mlsanity.com/paper/2206.11338/) (2022)
- [Implicit Session Contexts for Next-Item Recommendations](https://mlsanity.com/paper/2208.09076/) (2022)
- [Session-aware Linear Item-Item Models for Session-based Recommendation](https://mlsanity.com/paper/2103.16104/) (2021)
- [Introducing Self-Attention to Target Attentive Graph Neural Networks](https://mlsanity.com/paper/2107.01516/) (2021)
- [Data Augmentation Strategies for Improving Sequential Recommender Systems](https://mlsanity.com/paper/2203.14037/) (2022)
- [Adaptive Collaborative Filtering with Personalized Time Decay Functions for Financial Product Recommendation](https://mlsanity.com/paper/2308.01208/) (2023)
- [Positional encoding is not the same as context: A study on positional encoding for sequential recommendation](https://mlsanity.com/paper/2405.10436/) (2024)

## Abstract (arXiv)

Recommender systems (RSs) have been playing an increasingly important role for informed consumption, services, and decision-making in the overloaded information era and digitized economy. In recent years, session-based recommender systems (SBRSs) have emerged as a new paradigm of RSs. Different from other RSs such as content-based RSs and collaborative filtering-based RSs which usually model long-term yet static user preferences, SBRSs aim to capture short-term but dynamic user preferences to provide more timely and accurate recommendations sensitive to the evolution of their session contexts. Although SBRSs have been intensively studied, neither unified problem statements for SBRSs nor in-depth elaboration of SBRS characteristics and challenges are available. It is also unclear to what extent SBRS challenges have been addressed and what the overall research landscape of SBRSs is. This comprehensive review of SBRSs addresses the above aspects by exploring in depth the SBRS entities (e.g., sessions), behaviours (e.g., users' clicks on items) and their properties (e.g., session length). We propose a general problem statement of SBRSs, summarize the diversified data characteristics and challenges of SBRSs, and define a taxonomy to categorize the representative SBRS research. Finally, we discuss new research opportunities in this exciting and vibrant area.