Causal-Recommender-Systems

An index of causal inference based recommendation algorithms.

Our survey Causal Inference in Recommender Systems: A Survey and Future Directions has been accepted by ACM TOIS and is available on arxiv: link

Please cite our survey paper if this index is helpful.

@article{gao2022causal,
  title={Causal inference in recommender systems: A survey and future directions},
  author={Gao, Chen and Zheng, Yu and Wang, Wenjie and Feng, Fuli and He, Xiangnan and Li, Yong},
  journal={ACM Transactions on Information Systems},
  year={2022},
  publisher={ACM New York, NY}
}

Gao, Chen, Yu Zheng, Wenjie Wang, Fuli Feng, Xiangnan He, and Yong Li. "Causal inference in recommender systems: A survey and future directions." ACM Transactions on Information Systems (2022).

Table of Contents

• Causal Inference-based Recommendation for Addressing Data Bias
  • Popularity Bias
  • Clickbait Bias, Bias Amplification, Conformity Bias
  • Exposure Bias
• Causal Inference-based Recommendation for Addressing Data Missing and Noise
  • Data Missing
  • Data Noise
• Beyond-accuracy RecSys with Causal Inference
  • Explainability
  • Diversity
  • Fairness

Causal Inference-based Recommendation for Addressing Data Bias

Popularity Bias

Clickbait Bias, Bias Amplification, Conformity Bias

Exposure Bias

Causal Inference-based Recommendation for Addressing Data Missing and Noise

Data Missing

Data Noise

Beyond-accuracy RecSys with Causal Inference

Explainability

Diversity

Fairness