tutorial

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

Authors:

Charu AggarwalAuthors Info & Claims

SIGIR '22: Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval

Pages 3425 - 3428

https://doi.org/10.1145/3477495.3532685

Published: 07 July 2022 Publication History

Abstract

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.

References

[1]

Gabriel de Souza Pereira Moreira, Sara Rabhi, Ronay Ak, and Benedikt Schifferer. 2021. Tutorial on end-to-end session-based recommendation on GPU. In RecSys.

[2]

Hui Fang and et al. 2019. Tutorial on deep learning-based sequential recommender systems: concepts, algorithms, and evaluations. In ICWE, Vol. 11496. 574--577.

[3]

Hui Fang and et al. 2020. Deep learning for sequential recommendation: algorithms, influential factors, and evaluations. TOIS 39, 1 (2020), 1--42.

Digital Library

[4]

Balázs Hidasi, Alexandros Karatzoglou, and et al. 2016. Session-based recommendations with recurrent neural networks. In ICLR. 1--10.

[5]

Malte Ludewig, Noemi Mauro, and et al. 2021. Empirical analysis of session-based recommendation algorithms. UMUAI 31, 1 (2021), 149--181.

Digital Library

[6]

Ruihong Qiu, Zi Huang, and et al. 2022. Contrastive learning for representation degeneration problem in sequential recommendation. In WSDM. 813--823.

[7]

Massimo Quadrana and Paolo Cremonesi. 2018. Tutorial on sequence aware recommender systems. In RecSys.

[8]

Massimo Quadrana, Paolo Cremonesi, and Dietmar Jannach. 2018. Sequenceaware recommender systems. ACM Comput. Surv. 51, 4 (2018), 1--36.

Digital Library

[9]

Massimo Quadrana, Dietmar Jannach, and Paolo Cremonesi. 2019. Tutorial: sequence-aware recommender systems. In WWW (Companion Volume). 1316.

[10]

Steffen Rendle, Christoph Freudenthaler, and et al. 2010. Factorizing personalized markov chains for next-basket recommendation. In WWW. 811--820.

[11]

Shoujin Wang, Longbing Cao, Yan Wang, and et al. 2022. A survey on sessionbased recommender systems. ACM Comput. Surv. 54, 7 (2022), 1--38.

Digital Library

[12]

ShoujinWang, Liang Hu, Longbing Cao, and et al. 2018. Attention-based transactional context embedding for next-item recommendation. In AAAI. 2532--2539.

[13]

Shoujin Wang, Liang Hu, and et al. 2019. Sequential recommender systems: challenges, progress and prospects. In IJCAI. 6332--6338.

[14]

Shoujin Wang, Liang Hu, Yan Wang, and et al. 2021. Graph learning based recommender systems: A review. In IJCAI. 4644--4652.

[15]

Shu Wu, Yuyuan Tang, Yanqiao Zhu, and et al. 2019. Session-based recommendation with graph neural networks. In AAAI. 346--353.

[16]

Xiangyu Zhao, Liang Zhang, and et al. 2017. Deep reinforcement learning for list-wise recommendations. arXiv preprint arXiv:1801.00209 (2017), 1--10.

Cited By

Wang YZhu JDuan LLi ASun JWang CLi Z(2025)Disentangled Sparse Graph Attention Networks with Multi-Intent Fusion for Session-based RecommendationKnowledge-Based Systems10.1016/j.knosys.2025.113082311(113082)Online publication date: Feb-2025
https://doi.org/10.1016/j.knosys.2025.113082
Zhang ZYang BLu Y(2025)A Local context enhanced Consistency-aware Mamba-based Sequential Recommendation modelInformation Processing & Management10.1016/j.ipm.2025.10407662:3(104076)Online publication date: May-2025
https://doi.org/10.1016/j.ipm.2025.104076
Sun JZhu JWang CWang YNiu T(2025)Dual channel representation-learning with dynamic intent aggregation for session-based recommendationExpert Systems with Applications10.1016/j.eswa.2024.125273259(125273)Online publication date: Jan-2025
https://doi.org/10.1016/j.eswa.2024.125273
Show More Cited By

Index Terms

Sequential/Session-based Recommendations: Challenges, Approaches, Applications and Opportunities
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Recommender systems

Recommendations

News Session-Based Recommendations using Deep Neural Networks
DLRS 2018: Proceedings of the 3rd Workshop on Deep Learning for Recommender Systems

News recommender systems are aimed to personalize users experiences and help them to discover relevant articles from a large and dynamic search space. Therefore, news domain is a challenging scenario for recommendations, due to its sparse user profiling, ...
Inter-Session Modeling for Session-Based Recommendation
DLRS 2017: Proceedings of the 2nd Workshop on Deep Learning for Recommender Systems

In recent years, research has been done on applying Recurrent Neural Networks (RNNs) as recommender systems. Results have been promising, especially in the session-based setting where RNNs have been shown to outperform state-of-the-art models. In many of ...
Combining User-Based and Session-Based Recommendations with Recurrent Neural Networks
Neural Information Processing
Abstract
Recommender systems generate recommendations based on user profiles, which consist of past interactions of users with items. When user profiles are not available, session-based recommendation can be used instead to make predictions based on ...

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences

SIGIR '22: Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval

July 2022

3569 pages

ISBN:9781450387323

DOI:10.1145/3477495

General Chairs:
Enrique Amigo
UNED
,
Pablo Castells
UAM and Amazon
,
Julio Gonzalo
UNED
,
Program Chairs:
Ben Carterette
Spotify
,
J. Shane Culpepper
RMIT University
,
Gabriella Kazai
Waseda University

Copyright © 2022 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Sponsors

SIGIR: ACM Special Interest Group on Information Retrieval

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 07 July 2022

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Tutorial

Funding Sources

Australian Research Council

Conference

SIGIR '22

Sponsor:

SIGIR

SIGIR '22: The 45th International ACM SIGIR Conference on Research and Development in Information Retrieval

July 11 - 15, 2022

Madrid, Spain

Acceptance Rates

Overall Acceptance Rate 792 of 3,983 submissions, 20%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

32
Total Citations
View Citations
548
Total Downloads

Downloads (Last 12 months)198
Downloads (Last 6 weeks)15

Reflects downloads up to 20 Feb 2025

Other Metrics

View Author Metrics

Citations

Cited By

Wang YZhu JDuan LLi ASun JWang CLi Z(2025)Disentangled Sparse Graph Attention Networks with Multi-Intent Fusion for Session-based RecommendationKnowledge-Based Systems10.1016/j.knosys.2025.113082311(113082)Online publication date: Feb-2025
https://doi.org/10.1016/j.knosys.2025.113082
Zhang ZYang BLu Y(2025)A Local context enhanced Consistency-aware Mamba-based Sequential Recommendation modelInformation Processing & Management10.1016/j.ipm.2025.10407662:3(104076)Online publication date: May-2025
https://doi.org/10.1016/j.ipm.2025.104076
Sun JZhu JWang CWang YNiu T(2025)Dual channel representation-learning with dynamic intent aggregation for session-based recommendationExpert Systems with Applications10.1016/j.eswa.2024.125273259(125273)Online publication date: Jan-2025
https://doi.org/10.1016/j.eswa.2024.125273
Liu YQi JYu Y(2025)A Review on Deep Learning for Sequential Recommender Systems: Key Technologies and DirectionsBig Data10.1007/978-981-96-1024-2_22(305-318)Online publication date: 24-Jan-2025
https://doi.org/10.1007/978-981-96-1024-2_22
Zhang RWang HHe J(2024)HyperCLR: A Personalized Sequential Recommendation Algorithm Based on Hypergraph and Contrastive LearningMathematics10.3390/math1218288712:18(2887)Online publication date: 16-Sep-2024
https://doi.org/10.3390/math12182887
Wang LJin D(2024)A Time-Sensitive Graph Neural Network for Session-Based New Item RecommendationElectronics10.3390/electronics1301022313:1(223)Online publication date: 3-Jan-2024
https://doi.org/10.3390/electronics13010223
Song WXing X(2024)Dual-Tower Counterfactual Session-Aware Recommender SystemEntropy10.3390/e2606051626:6(516)Online publication date: 14-Jun-2024
https://doi.org/10.3390/e26060516
Celik EIlhan Omurca S(2024)Skip-Gram and Transformer Model for Session-Based RecommendationApplied Sciences10.3390/app1414635314:14(6353)Online publication date: 21-Jul-2024
https://doi.org/10.3390/app14146353
El-Shaer EMcKee GHamdy A(2024)The LSTM-EMPG Model for Next Basket Recommendation in E-commerceInternational Journal of Information and Communication Sciences10.11648/j.ijics.20240901.129:1(9-23)Online publication date: 15-Jul-2024
https://doi.org/10.11648/j.ijics.20240901.12
Huynh TNguyen TNguyen TNguyen PYin HNguyen QNguyen T(2024)Certified Unlearning for Federated RecommendationACM Transactions on Information Systems10.1145/3706419Online publication date: 2-Dec-2024
https://doi.org/10.1145/3706419
Show More Cited By

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Figures

Tables

Media

View Table of Conten