tutorial

User Behavior Modeling with Deep Learning for Recommendation: Recent Advances

Authors:

Hao WangAuthors Info & Claims

RecSys '23: Proceedings of the 17th ACM Conference on Recommender Systems

Pages 1286 - 1287

https://doi.org/10.1145/3604915.3609496

Published: 14 September 2023 Publication History

Abstract

User Behavior Modeling (UBM) plays a critical role in user interest learning, and has been extensively used in recommender systems. The exploration of key interactive patterns between users and items has yielded significant improvements and great commercial success across a variety of recommendation tasks. This tutorial aims to offer an in-depth exploration of this evolving research topic. We start by reviewing the research background of UBM, paving the way to a clearer understanding of the opportunities and challenges. Then, we present a systematic categorization of existing UBM research works, which can be categorized into four different directions including Conventional UBM, Long-Sequence UBM, Multi-Type UBM, and UBM with Side Information. To provide an expansive understanding, we delve into each category, discussing representative models while highlighting their respective strengths and weaknesses. Furthermore, we elucidate on the industrial applications of UBM methods, aiming to provide insights into the practical value of existing UBM solutions. Finally, we identify some open challenges and future prospects in UBM. This comprehensive tutorial serves to provide a solid foundation for anyone looking to understand and implement UBM in their research or business.

References

[1]

Wei Guo, Chang Meng, Enming Yuan, Zhicheng He, Huifeng Guo, Yingxue Zhang, Bo Chen, Yaochen Hu, Ruiming Tang, Xiu Li, 2023. Compressed Interaction Graph based Framework for Multi-behavior Recommendation. In Proceedings of the ACM Web Conference 2023. 960–970.

Digital Library

[2]

Zhicheng He, Weiwen Liu, Wei Guo, Jiarui Qin, Yingxue Zhang, Yaochen Hu, and Ruiming Tang. 2023. A Survey on User Behavior Modeling in Recommender Systems. arXiv preprint arXiv:2302.11087 (2023).

[3]

Balázs Hidasi, Massimo Quadrana, and Alexandros Karatzoglou et al. 2016. Parallel recurrent neural network architectures for feature-rich session-based recommendations. In RecSys.

[4]

Yifan Hu, Yehuda Koren, and Chris Volinsky. 2008. Collaborative Filtering for Implicit Feedback Datasets. In ICDM.

[5]

Yidan Hu, Yong Liu, Chunyan Miao, and Yuan Miao. 2022. Memory Bank Augmented Long-tail Sequential Recommendation. In Proceedings of the 31st ACM International Conference on Information & Knowledge Management. 791–801.

Digital Library

[6]

Chenyi Lei, Yong Liu, Lingzi Zhang, Guoxin Wang, Haihong Tang, Houqiang Li, and Chunyan Miao. 2021. Semi: A sequential multi-modal information transfer network for e-commerce micro-video recommendations. In Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining. 3161–3171.

Digital Library

[7]

Yi Li, Jieming Zhu, Weiwen Liu, Liangcai Su, Guohao Cai, Qi Zhang, Ruiming Tang, Xi Xiao, and Xiuqiang He. 2022. PEAR: Personalized Re-ranking with Contextualized Transformer for Recommendation. In Companion Proceedings of the Web Conference 2022. 62–66.

Digital Library

[8]

Weiwen Liu, Jiarui Qin, Ruiming Tang, and Bo Chen. 2022. Neural Re-ranking for Multi-stage Recommender Systems. In Proceedings of the 16th ACM Conference on Recommender Systems. 698–699.

Digital Library

[9]

Weiwen Liu, Yunjia Xi, Jiarui Qin, Xinyi Dai, Ruiming Tang, Shuai Li, Weinan Zhang, and Rui Zhang. 2023. Personalized Diversification for Neural Re-ranking in Recommendation.

[10]

Weiwen Liu, Yunjia Xi, Jiarui Qin, Fei Sun, Bo Chen, Weinan Zhang, Rui Zhang, and Ruiming Tang. 2022. Neural Re-ranking in Multi-stage Recommender Systems: A Review. In International Joint Conference on Artificial Intelligence.

Digital Library

[11]

Qi Pi, Guorui Zhou, and Yujing Zhang et al. 2020. Search-based user interest modeling with lifelong sequential behavior data for click-through rate prediction. In CIKM.

[12]

Jiarui Qin, Weinan Zhang, and Xin Wu et al. 2020. User behavior retrieval for click-through rate prediction. In SIGIR.

[13]

Jiarui Qin, Weinan Zhang, Rong Su, Zhirong Liu, Weiwen Liu, Guangpeng Zhao, Hao Li, Ruiming Tang, Xiuqiang He, and Yong Yu. 2023. Learning to Retrieve User Behaviors for Click-Through Rate Estimation. ACM Transactions on Information Systems (2023).

[14]

Yunjia Xi, Weiwen Liu, Jieming Zhu, Xilong Zhao, Xinyi Dai, Ruiming Tang, Weinan Zhang, Rui Zhang, and Yong Yu. 2022. Multi-Level Interaction Reranking with User Behavior History. In Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval. 1336–1346.

Digital Library

[15]

Enming Yuan, Wei Guo, Zhicheng He, Huifeng Guo, Chengkai Liu, and Ruiming Tang. 2022. Multi-behavior sequential transformer recommender. In Proceedings of the 45th international ACM SIGIR conference on research and development in information retrieval. 1642–1652.

Digital Library

[16]

Hengyu Zhang, Enming Yuan, Wei Guo, Zhicheng He, Jiarui Qin, Huifeng Guo, Bo Chen, Xiu Li, and Ruiming Tang. 2022. Disentangling Past-Future Modeling in Sequential Recommendation via Dual Networks. In Proceedings of the 31st ACM International Conference on Information & Knowledge Management. 2549–2558.

Digital Library

[17]

Weinan Zhang, Jiarui Qin, and Wei Guo et al. 2021. Deep Learning for Click-Through Rate Estimation. In IJCAI.

[18]

Yixin Zhang, Yong Liu, Hao Xiong, Yi Liu, Fuqiang Yu, Wei He, Yonghui Xu, Lizhen Cui, and Chunyan Miao. 2023. Cross-Domain Disentangled Learning for E-Commerce Live Streaming Recommendation.

[19]

Yixin Zhang, Yong Liu, Yonghui Xu, Hao Xiong, Chenyi Lei, Wei He, Lizhen Cui, and Chunyan Miao. 2022. Enhancing sequential recommendation with graph contrastive learning. In Proceedings of the 31st International Joint Conference on Artificial Intelligence.

[20]

Wei Zhou, Yong Liu, Min Li, Yu Wang, Zhiqi Shen, Liang Feng, and Zexuan Zhu. 2023. Dynamic Multi-Objective Optimization Framework With Interactive Evolution for Sequential Recommendation. IEEE Transactions on Emerging Topics in Computational Intelligence (2023).

Cited By

Lin JDai XXi YLiu WChen BZhang HLiu YWu CLi XZhu CGuo HYu YTang RZhang W(2024)How Can Recommender Systems Benefit from Large Language Models: A SurveyACM Transactions on Information Systems10.1145/3678004Online publication date: 13-Jul-2024
https://dl.acm.org/doi/10.1145/3678004
Zhang HPan JLiu DJiang JLi XHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)Deep Pattern Network for Click-Through Rate PredictionProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657777(1189-1199)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.1145/3626772.3657777
Yan SZhao CShen NJiang S(2024)Position-Awareness and Hypergraph Contrastive Learning for Multi-Behavior Sequence RecommendationIEEE Access10.1109/ACCESS.2024.351398212(185958-185970)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3513982

Index Terms

User Behavior Modeling with Deep Learning for Recommendation: Recent Advances
1. Human-centered computing
  1. Human computer interaction (HCI)
    1. HCI design and evaluation methods
      1. User models
2. Information systems

Index terms have been assigned to the content through auto-classification.

Recommendations

An effective recommendation based on user behaviour: a hybrid of sequential pattern of user and attributes of product

Recommender system is a promising technology for companies to present personalised offers to their customers. But this technology suffers from sparsity problem. In addition, most researches are based on explicit rating. But most users do not spend time ...
Modeling user exposure with recommendation influence
SAC '20: Proceedings of the 35th Annual ACM Symposium on Applied Computing

Recommender systems learn users' preferences from their explicit feedback, such as ratings, and implicit feedback, such as purchases. Learning a user's preference from implicit feedback is difficult since inaction in such data can be because of either ...
A Clustering-Based Collaborative Filtering Recommendation Algorithm via Deep Learning User Side Information
Web Information Systems Engineering – WISE 2020
Abstract
Collaborative filtering (CF) is a widely used recommendation approach that relies on user-item ratings. However, the natural sparsity of user-item ratings can be problematic in many domains, limiting the ability to produce accurate and effective ...

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences

RecSys '23: Proceedings of the 17th ACM Conference on Recommender Systems

September 2023

1406 pages

ISBN:9798400702419

DOI:10.1145/3604915

Copyright © 2023 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 the author(s) 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

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 14 September 2023

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Qualifiers

Tutorial
Research
Refereed limited

Conference

RecSys '23

Sponsor:

RecSys '23: Seventeenth ACM Conference on Recommender Systems

September 18 - 22, 2023

Singapore, Singapore

Acceptance Rates

Overall Acceptance Rate 254 of 1,295 submissions, 20%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

3
Total Citations
View Citations
301
Total Downloads

Downloads (Last 12 months)169
Downloads (Last 6 weeks)21

Reflects downloads up to 27 Dec 2024

Other Metrics

View Author Metrics

Citations

Cited By

Lin JDai XXi YLiu WChen BZhang HLiu YWu CLi XZhu CGuo HYu YTang RZhang W(2024)How Can Recommender Systems Benefit from Large Language Models: A SurveyACM Transactions on Information Systems10.1145/3678004Online publication date: 13-Jul-2024
https://dl.acm.org/doi/10.1145/3678004
Zhang HPan JLiu DJiang JLi XHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)Deep Pattern Network for Click-Through Rate PredictionProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657777(1189-1199)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.1145/3626772.3657777
Yan SZhao CShen NJiang S(2024)Position-Awareness and Hypergraph Contrastive Learning for Multi-Behavior Sequence RecommendationIEEE Access10.1109/ACCESS.2024.351398212(185958-185970)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3513982

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.

HTML Format

View this article in HTML Format.

Media

Figures

Other

Tables

View Table of Contents