Sequential Recommender Systems: Challenges, Progress and Prospects

By Shoujin Wang, Liang Hu, Yan Wang, Longbing Cao, Quan Z. Sheng, Mehmet Ali Orgun. International Joint Conference on Artificial Intelligence, 2019

517 citations recorded by the source corpus.

The emerging topic of sequential recommender systems (SRSs) has attracted increasing attention in recent years. Different from the conventional recommender systems (RSs) including collaborative filtering and content-based filtering, SRSs try to understand and model the sequential user behaviors, the interactions between users and items, and the evolution of users’ preferences and item popularity over time. SRSs involve the above aspects for more precise characterization of user contexts, intent and goals, and item consumption trend, leading to more accurate, customized and dynamic recommendations. In this paper, we provide a systematic review on SRSs. We first present the characteristics of SRSs, and then summarize and categorize the key challenges in this research area, followed by the corresponding research progress consisting of the most recent and representative developments on this topic. Finally, we discuss the important research directions in this vibrant area.

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

View Paper: https://www.semanticscholar.org/paper/d837642802ffc85e193694f94f7499276864648e

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. Hongzhi Yin
2. Tong Chen
3. Xin Xia
4. Junliang Yu
5. Fuli Feng
6. Xiangnan He
7. Zi-Liang Huang
8. Fei Sun

In Information Systems

1. Hongzhi Yin
2. Tong Chen
3. Xin Xia
4. Junliang Yu
5. Fuli Feng
6. Xiangnan He
7. Zi-Liang Huang
8. Bin Cui

In Management Science and Operations Research

1. Xiangnan He
2. Fuli Feng
3. Hongzhi Yin
4. Wenjie Wang
5. Fei Sun
6. Gao Cong
7. Bin Cui
8. Jie Lu

Citations in the corpus

• A Survey on Session-based Recommender Systems (2019, methodology): “, long-range dependencies [141] between two interactions that are far from each other in a session or high-order dependencies [119] across multiple interactions in a session.”
• Adversarial and Contrastive Variational Autoencoder for Sequential Recommendation (2021, background): “Different from conventional RSs, SRSs consider the sequential context by taking the prior sequential interactions into account, which effectively models users’ dynamic preference and items’ popularity [26].”
• Global Context Enhanced Graph Neural Networks for Session-based Recommendation (2020, methodology): “However, similar to MC-based methods, RNN-based methods focus on modeling the sequential transitions of adjacent items [20] to infer user preference via the chronology of the given sequence, and thus cannot model the complex item-transition patterns (e.”
• Artificial intelligence in recommender systems (2020, background): “RNNs have emerged as a new direction known as session-based recommender systems or sequential recommender systems where the real-time recommendation is refined according to the historical sequential data [96, 97].”
• Cross-Domain Recommendation: Challenges, Progress, and Prospects (2021, background): “…to further improve the recommendation performance, becomes the first promising prospect in CDR. Sequential CDR. Sequential recommendation has gained much attention since it can suggest items to users by modeling the sequential dependencies over the user-item interactions [Wang et al. , 2019a].”
• TALLRec: An Effective and Efficient Tuning Framework to Align Large Language Model with Recommendation (2023, background): “Our setup is close to sequential recommendation which aims to infer the user’s next preference based on the historical sequence of user behaviours [10, 49].”
• Contrastive Learning for Sequential Recommendation (2022, background): “However, in the real world, sometimes the order of users’ interactions are in a flexible manner [41, 44], due to various unobservable external factors [7].”
• Self-Supervised Learning for Recommender Systems: A Survey (2022, background): “However, this assumption is probably problematic because in the real world many unobserved external factors can impact the item order [66] and different item orders may actually correspond to the same user intent.”