A Survey on Session-based Recommender Systems

By Shoujin Wang, Longbing Cao, Yan Wang, Quan Z. Sheng. ACM Computing Surveys, 2019

614 citations recorded by the source corpus.

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 that 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.

Subfields: Artificial Intelligence, Information Systems, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/56107ce1a3f5d6c0fdd32fa7ab95329ec207ba9c

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Xiangnan He
2. Hongzhi Yin
3. Xiang Wang
4. Junliang Yu
5. Xin Xia
6. Yong Li
7. Chen Gao
8. Meng Wang

In Information Systems

1. Hongzhi Yin
2. Xiangnan He
3. Xiang Wang
4. Junliang Yu
5. Xin Xia
6. Meng Wang
7. Yong Li
8. Chen Gao

In Management Science and Operations Research

1. Xiang Wang
2. Meng Wang
3. Xiangnan He
4. Le Wu
5. Kun Zhang
6. Hongzhi Yin
7. M. M. Afsar
8. Fei Sun

Citations in the corpus

• A Survey on Accuracy-Oriented Neural Recommendation: From Collaborative Filtering to Information-Rich Recommendation (2021, background): “I R ] 27 D ec 2021 recommendation [15].”
• Graph Neural Networks in Recommender Systems: A Survey (2020, background): “[149] propose a comprehensive survey in the sessionbased recommendations.”
• Self-Supervised Hypergraph Convolutional Networks for Session-based Recommendation (2020, background): “Session-based recommendation (SBR) is an emerging recommendation paradigm, where long-term user profiles are usually not available (Wang, Cao, and Wang 2019; Guo et al. 2019).”
• Reinforcement Learning based Recommender Systems: A Survey (2021, methodology): “In another related survey [42], RL is considered as a method for session-based RSs [46, 51, 52].”
• A Survey of Graph Neural Networks for Recommender Systems: Challenges, Methods, and Directions (2021, background): “• There are other surveys on specific topics of recommendation, such as Reference [175] for session-based recommendation, References [30, 59] for knowledge-graph based recommendation, Reference [26] for explainable recommendation, Reference [191] for news recommendation.”
• A Survey of Graph Neural Networks for Social Recommender Systems (2022, background): “For this reason, time-aware recommender systems have attracted increasing attention in recent years [95].”