Introduction

Given the sheer volume of contemporary e-commerce applications, recommender systems (RSs) have gained significant attention in both academia and industry. However, traditional cloud-based RSs face inevitable challenges, such as resource-intensive computation, reliance on network access, and privacy breaches. In response, a new paradigm called on-device recommender systems (ODRSs) has emerged recently in various industries like Taobao, Google, and Kuaishou. ODRSs unleash the computational capacity of user devices with lightweight recommendation models tailored for resource-constrained environments, enabling real-time inference with users’ local data. This tutorial aims to systematically introduce methodologies of ODRSs, including (1) an overview of existing research on ODRSs; (2) a comprehensive taxonomy of ODRSs, where the core technical content to be covered span across three major ODRS research directions, including on-device deployment and inference, on-device training, and privacy/security of ODRSs; (3) limitations and future directions of ODRSs. This tutorial expects to lay the foundation and spark new insights for follow-up research and applications concerning this new recommendation paradigm.

Schedule

  1. Section 1: Welcome and Introduction
    1. Overview of Recommender Systems (RSs)
    2. On-Device Recommender Systems (ODRSs): Background and Applications
  2. Section 2: Definition and Taxonomy of ODRSs
    1. Definition of On-Device Recommendation Tasks
    2. Categorization of Existing ODRSs
  3. Section 3: A Review of ODRSs
    1. On-Device Deployment and Inference
    2. On-Device Training
    3. Privacy and Security
  4. Section 4: Limitations and New Trends
    1. Open Challenges for Existing ODRSs
    2. Emerging Research Directions
  5. Section 5: Open Discussions
    1. Questions and Answers
    2. Reflections, Suggestions, and Link to Our Resources

Presenters' Biography

Prof. Hongzhi Yin

Prof. Hongzhi Yin works as an ARC Future Fellow, Full Professor, and Director of the Responsible Big Data Intelligence Lab (RBDI) at The University of Queensland, Australia. He has published 260+ papers with an H-index of 66, making notable contributions to recommendation systems, graph learning, decentralized learning, and edge intelligence. His research has won 8 international and national Best Paper Awards, including Best Paper Award (Honorable Mention) at WSDM 2023, Best Paper Award at ICDE 2019, and Best Student Paper Award at DASFAA 2020. He has received the prestigious 2023 AIPS Young Tall Poppy Science Awards, 2022 IEEE Computer Society AI's 10 to Watch, 2021 ARC Future Fellowship, and 2016 ARC DECRA Fellowship. He has been an SPC or area chair for many top conferences, such as WWW, IJCAI, AAAI, KDD, SIGIR, WSDM, ICDE, CIKM, and DASFAA. Prof. Yin has rich lecture experience and taught five relevant courses, such as information retrieval and web search, data mining, social media analytics, and responsible data science. He won the Faculty Teaching and Learning Excellence Award 2022 and the University Teaching and Learning Excellence Award 2022 (finalist). In addition, he has delivered 20+ keynotes and tutorials at the top international conferences like DASFAA'23, WWW'22, BESC'22, ADMA'19, WWW'17, and KDD'17.

Dr. Tong Chen

Dr. Tong Chen is a senior lecturer at The University of Queensland, and an awardee of the 2023 Discovery Early Career Researcher Award from the Australian Research Council (ARC). Dr. Chen's research on lightweight and on-device recommender systems has been published on top-tier international venues such as KDD, SIGIR, WWW, TKDE, WSDM, TNNLS, TOIS, and CIKM. Dr. Chen has ample track records in lecturing, witnessed by his course design and delivery experience in business analytics, teaching experience in social media analytics, as well as invited talks on cutting-edge recommender systems at the DASFAA'23 Tutorial, WWW'22 Tutorial, and ICDM'20 NeuRec Workshop.

Liang Qu

Mr. Liang Qu is currently pursuing his Ph.D. under a joint program between The University of Queensland and Southern University of Science and Technology. In 2017, he earned his B.E. in Applied Physics from the South China University of Technology, followed by an M.S. in Computer Science in 2019 from the Harbin Institute of Technology. His research work has been published on top data mining venues such as KDD, SIGIR, WWW, and TOIS. In addition, he has been an PC and/or reviewer for many top venues, such as KDD, WWW, CIKM, and VLDB. His research interest primarily lies in the development of lightweight, privacy-preserving, and trustworthy recommender systems, such as federated recommendation and on-device recommendation.

Prof. Bin Cui

Prof. Bin Cui is a Cheung Kong Distinguished Professor, Vice Dean of the School of Computer Science at Peking University, and Director of Peking University-Tencent Joint Innovation Laboratory. His research interests include recommendation and search system architectures, query and index techniques, big data management and mining, and distributed machine learning systems. He has served on the Technical Program Committee of various international conferences, including SIGMOD, VLDB, ICDE, WWW, KDD, and as Area Chair of ICDE 2011\&2018, Demo Co-Chair of ICDE 2014, Area Chair of VLDB 2014, PC Co-Chair of APWeb 2015, WAIM 2016 and DASFAA 2020. He serves as Vice Chair of Technical Committee on Database China Computer Federation (CCF) and Trustee Board Member of VLDB Endowment. He is also on the Editorial Board of Distributed and Parallel Databases, Journal of Computer Science and Technology, and SCIENCE CHINA Information Sciences, and was an associate editor of IEEE Transactions on Knowledge and Data Engineering (TKDE) and VLDB Journal. He was awarded Microsoft Young Professorship Award (MSRA 2008), CCF Young Scientist Award (2009), Second Prize of Natural Science Award of MOE China (2014), and appointed as Cheung Kong Distinguished Professor by MOE in 2016.