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What is Causal Inference in Recommendation Systems about?

This document surveys the integration of causal inference into recommender systems, highlighting its importance for addressing issues such as fairness, explainability, and robustness. It reviews foundational concepts, causal methods, and applications in recommendation, while also identifying open problems and future research directions. The survey distinguishes itself by bridging the gap between causal inference and recommender systems, providing a comprehensive understanding of their intersection.

Author

Londonbell

Language

EN