IJCAI 2025 Workshop

Recommendation systems are foundational to modern digital platforms, influencing user experiences across diverse domains such as e-commerce, social media, and streaming services. Despite their widespread use, traditional recommendation models predominantly rely on correlation-based learning, which can inadvertently lead to suboptimal, biased, or even unfair recommendations. In contrast, causal learning offers a paradigm shift, enabling the development of more reliable, equitable, and interpretable systems by explicitly modeling cause-effect relationships. By understanding the underlying mechanisms that drive user behavior, causal models hold the potential to unlock deeper insights and more personalized recommendations.

This workshop aims to bring together leading researchers and practitioners from the fields of machine learning, causal inference, and recommender systems to explore the promising intersection of causal learning and recommendation technologies. We invite contributions that showcase innovative approaches, cutting-edge research, and practical solutions, demonstrating how causal reasoning can address critical challenges such as bias, fairness, and interpretability in recommendation systems.

Dr. Rong Jin

Dr. Jin is a Research Director at Meta Platforms, Inc., working on large models for ad recommendation. Prior to Meta, he served as a professor at Michigan State University (2003 – 2014), a vice president of Alibaba Group (2014 – 2022), and a vice president at Twitter (2022 – 2023). His research interest includes statistical machine learning and its application to large-scale data management. Dr. Jin received a Ph.D. in Computer Science from Carnegie Mellon University. He received Best Paper Award from the 25th Conference of Learning Theory (COLT), and NSF Career Award, 2007. He is the Associate Editor of ACM Transactions on Knowledge Discovery from Data and IEEE Transactions on Pattern Analysis and Machine Intelligence. His total citation is over 36,000, and H-index is 96, according to GoogleScholar.

This workshop aims to bring together leading researchers and practitioners to explore the promising intersection of causal learning and recommendation technologies. Below is a non-exhaustive list of topic categories and subcategories we aim to explore:

We are pleased to announce the accepted papers for the IJCAI 2025 Workshop on Causal Learning for Recommendation Systems. All accepted papers could be found through the link below.

Location: Room 520F