IJCAI 2025 Workshop
Causal Learning for Recommendation Systems
Montreal, Canada
August 16–22, 2025
Submit Your PaperIntroduction
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.
Important Dates
Paper Submission Deadline
May 9, 2025
Notification of Acceptance
June 6, 2025
Camera-ready Submission
June 20, 2025
Workshop Date
August 16–22, 2025
Call For Papers
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:
Causal Inference for Recommender Systems
- Integrating causal models to improve recommendation quality
- Modeling cause-effect relationships in user behavior
- Causal discovery techniques for system design
LLM Applications
- Causal learning helps LLMs generate disentangled representations
- LLMs can discover causal relationships from textual data
- Dynamic causal inference in sequential tasks
Bias and Fairness
- Identifying and mitigating biases using causal frameworks
- Fairness-aware recommendation models
- Evaluating fairness using counterfactuals
Interpretability and Transparency
- Explaining recommendations through causal reasoning
- Causal explanations of user-item interactions
- Methods for interpretable recommendation models
Counterfactual Learning
- Counterfactual analysis for recommendation effectiveness
- Generating personalized counterfactuals
- Measuring intervention impacts
Causal Reinforcement Learning
- Combining causal inference with reinforcement learning
- Adaptive recommendation strategies
- Dynamic recommendation systems
Practical Applications
- Real-world applications in e-commerce and social media
- Large-scale deployment case studies
- Industry lessons and best practices
Ethical Implications
- Ethical concerns and trade-offs
- Addressing societal impacts and biases
- Incorporating ethical considerations
Submission
Paper Format
- Please use the official IJCAI format
- Submit anonymous manuscripts (double-blind paper review)
- We accept papers of pages ≥4 (reference and appendix excluded)
Schedule
This is a tentative schedule and subject to change
Welcome and Opening Remarks
Welcome and introduction by workshop organizers
Invited Keynote 1
To be announced
Contributed Paper Session 1
Paper presentations
Coffee Break
Invited Keynote 2
To be announced
Lunch Break
Contributed Paper Session 2
Paper presentations
Poster Session and Networking Break
Interactive discussions with poster presenters
Closing Remarks
Concluding remarks and best paper award announcement
Committee
Organizing Committee
Program Committee
To Be Announced
The Program Committee is currently being formed. Please check back later for updates.