ICDM’23 Workshop

Graph Machine Learning for Recommender Systems


Recent years have witnessed the fast development of the emerg- ing topic of Graph Machine Learning for Recommender Systems (GML4Rec). GML4Rec employs advanced graph machine learning approaches to model users’ preferences and intentions on items as well as items’ characteristics for improving the recommenda- tion performance. Different from other RS approaches, including content-based filtering and collaborative filtering, GML4Rec ap- proaches are built on graphs where the important objects, e.g., users, items and attributes, are either explicitly or implicitly con- nected. With the rapid development of graph learning techniques such as GNNs, exploring and exploiting the homogeneous or het- erogeneous relations in graphs is becoming a promising direction for building more effective RS. Meanwhile, by matching the demon- strated challenges to the research progress already achieved, we have some open research directions need to be further explored, e.g., interpretability, fairness, and large-scale adaptability for GML4Rec. Therefore, we propose the GML4Rec workshop at ICDM’23, which provides a venue to gather both the academia and industry re- searchers/practitioners to present the recent progress of GML4Rec


In this workshop, we aim to discuss the latest research advances in the the- oretical foundations and practical application methods of GML4Rec. We invite submissions that focus on recent advances in the re search/development of GML4Rec along with their applications. Theory and methodology papers are welcome from any of the following areas, including but not limited to:
• Graph machine learning enhanced collaborative filtering
• Graph machine learning for sequential recommendation
• Graph machine learning for Recommender Systems by incor porating side information
• Graph machine learning strategies/algorithms for Recommender Systems
• Graph machine learning architectures for Recommender Sys tems
• Graph machine learning for self-evolutionary Recommender Systems
• Graph machine learning for explainable/interpretable Recom mender Systems
• Graph machine learning for robust/fair Recommender Systems
• Online graph machine learning based Recommender Systems

and applications papers focused on but not limited to:
• E-commerce platforms
• Streaming platforms
• Social networks
• Food and beverage


We invite the submission of regular research papers (max 8 pages plus 2 extra pages), including all content and references. Submissions must be in PDF format, and formatted according to the new Standard IEEE Conference Proceedings Template. We especially invite submissions for dedicated efforts for developing more advanced techniques that are able to advance the technologies of graph construction, information propagation, and side attributes aggregation in GML4Rec, and further promote the research activities in interpretability, robustness, fairness, self-evolution, and large-scale adaptability for realizing desired GML4Rec. For more questions about the workshop and submissions, please send email to ruochen1003@foxmail.com . The submission link is GML4Rec2023


● Workshop papers submission: September 15, 2023

● Notification of workshop papers acceptance to authors: September 24, 2023

● Camera-ready deadline and copyright form: October 1, 2023

● Workshop Day: December 4, 2023



Philip S. Yu



Xiangnan He



Chuan Shi



Hongzhi Yin

University of Queensland

PC member

We plan to invite the following researchers to serve as the PC members for the GML4Rec workshop at ICDM’23.
• Chao Huang, chuang@cs.hku.hk, The University of Hong Kong
• Xiao Wang, xiaowang@bupt.edu.cn, Beihang University
• Yanjie Fu, yanjie.fu@ucf.edu, University of Central Florida
• Chaozhuo Li, cli@microsoft.com, MSRA
• Liang Chen, chenliang6@mail.sysu.edu.cn, Sun Yat-Sen Uni versity
• Jianliang Gao, gaojianliang@csu.edu.cn, Central South Univer sity
• Hao Peng, penghao@buaa.edu.cn, Beihang University
• Yan Zhao, yanz@cs.aau.dk, Aalborg Universitet
• Dongxiao He, hedongxiao@tju.edu.cn, Tianjin University
• Yanwei Yu, yuyanwei@ouc.edu.cn, The Ocean University of China
• Guixiang Ma, uixiang.ma@intel.com, Intel Labs
• Yingtong Dou, vidou@visa.com, Visa Research
• Yuan Fang, yfang@smu.edu.sg, Singapore Management Uni versity
• Xiang Wang, xiangwang@ustc.edu.cn, University of Science and Technology of China
• Hechang Chen, chenhc@jlu.edu.cn, Jilin University
• Xiaotian Han, han@tamu.edu, Texas A&M Unversity
• Junbo Zhang, msjunbozhang@outlook.com, JD Intelligent Cities Research
• Lingfei Wu, lwu@email.wm.edu, Pinterest
• Beatrice Bevilacqua, bbevilac@purdue.edu, Purdue University
• Harry Shomer, shomerha@msu.edu, Michigan State University



Senzhang Wang

Central South University


Di Jin

Tianjin University


Changdong Wang

Sun Yat-sen University


Shirui Pan

Griffith University