CIRES Postdoctoral researcher, Dr Junliang Yu, and CIRES CIs, Dr Rocky Chen and A/Prof Hongzhi Yin, presented a tutorial on Self-Supervised Learning for Recommendation: Foundations, Methods and Prospects at 28th International Conference on Database Systems for Advanced Applications (DASFAA 2023), April 17-20, 2023, Tianjin, China.
Brief outline of the tutorial:
Recommender systems have become a necessity in this Internet era to offer personalization. However, in contrast to the increasing ease of model building and deployment, the lack of user behavioral data still remains a major pain point for modern recommender systems that constantly compromises recommendation performance. Recently, self-supervised learning (SSL), which can enable training on massive unlabeled data with automatic data annotation, has achieved tremendous success in many fields and been applied to an ever-expanding range of applications including recommendation. Many recent studies have demonstrated that all kinds of recommendation models can be significantly improved through learning with well-designed self-supervised tasks and data augmentations. In this tutorial, we will provide a panorama of the research efforts on self-supervised recommendation. Specifically, the content includes: (1) foundations and overview of self-supervised recommendation; (2) a comprehensive taxonomy of existing self-supervised recommendation methods; (3) how to apply SSL to various recommendation scenarios; (4) Challenges and future research directions.
Junliang Yu is a postdoctoral research fellow in the ARC Training Centre for Information Resilience (CIRES) at The University of Queensland. His research interests include recommender systems, social media analytics, deep learning on graphs, and self-supervised learning. He has 10+ publications on top-tier international venues such as KDD, WWW, ICDM, CIKM, AAAI, SIGIR, VLDBJ, and TKDE. He has been actively providing professional services to many toptier conferences/journals such as AAAI, CIKM, IJCAI, etc. He has rich lecture experience and tutored one relevant course of social media analytics, and also has made oral presentations on multiple top-tier conferences.
Dr. Tong Chen is a lecturer with the Data Science Discipline at The University of Queensland. He received his PhD degree in Computer Science from The University of Queensland in 2020. Dr. Chen’s research interests include data mining, machine learning, business intelligence, recommender systems, and predictive analytics. He has 60+ publications on top-tier international venues such as KDD, SIGIR, ICDE, AAAI, IJCAI, ICDM, WWW, TKDE, IJCAI, TOIS, and CIKM. He has been actively providing professional services to over 20 world-leading international conferences/journals in the fields of data mining, information retrieval and AI. Dr. Chen has ample track records in lecturing, witnessed by his course design and delivery experience in business analytics, full-course teaching experience in social media analytics and database systems, as well as invited talks on cutting-edge recommender systems at the WWW’22 Tutorial, ICDM’20 NeuRec Workshop, Beihang University, and Zhejiang University.
Dr. Hongzhi Yin works as ARC Future Fellow and associate professor with The University of Queensland, Australia. He received his doctoral degree from Peking University in July 2014. His current main research interests include recommender systems, graph embedding and mining, chatbots, social media analytics and mining, edge machine learning, trustworthy machine learning, decentralized and federated learning, and smart healthcare. He has published 220+ papers with Hindex 52, including 22 most highly cited publications in Top 1% (CNCI) venues such as KDD, SIGIR, WWW, WSDM, SIGMOD, VLDB, ICDE, AAAI, TKDE,etc. He has won 6 Best Paper Awards such as Best Paper Award at ICDE 2019, Best Student Paper Award at DASFAA 2020, and Best Paper Award Nomination at ICDM 2018. Dr. Yin has rich lecture experience and taught 5 relevant courses such as information retrieval and web search, data mining, social media analytics, and responsible data science. He was nominated as Most Effective Teacher of EAIT Faculty in The University of Queensland for 2020, 2021 and 2022. He has delivered 12 keynotes, invited talks and tutorials at the top international conferences such as tutorials at WWW 2017, KDD 2017 and WWW 2022.