/Causal-Recommender-Systems

An index of causal inference based recommendation algorithms.

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

Name Paper Causal Inference Method Venue Year Code
PD Zhang, Y., Feng, F., He, X., Wei, T., Song, C., Ling, G., & Zhang, Y. (2021, July). Causal intervention for leveraging popularity bias in recommendation. In Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval (pp. 11-20). Backdoor Adjustment SIGIR 2021 Python/TF
MACR Wei, T., Feng, F., Chen, J., Wu, Z., Yi, J., & He, X. (2021, August). Model-agnostic counterfactual reasoning for eliminating popularity bias in recommender system. In Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining (pp. 1791-1800). Counterfactual Inference KDD 2021 Python/TF

Clickbait Bias, Bias Amplification, Conformity Bias

Name Paper Causal Inference Method Venue Year Code
CR Wang, W., Feng, F., He, X., Zhang, H., & Chua, T. S. (2021, July). Clicks can be cheating: Counterfactual recommendation for mitigating clickbait issue. In Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval (pp. 1288-1297). Counterfactual Inference SIGIR 2021 Python/Torch
DecRS Wang, W., Feng, F., He, X., Wang, X., & Chua, T. S. (2021, August). Deconfounded recommendation for alleviating bias amplification. In Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining (pp. 1717-1725). Backdoor Adjustment KDD 2021 Python/Torch
DICE Zheng, Y., Gao, C., Li, X., He, X., Li, Y., & Jin, D. (2021, April). Disentangling user interest and conformity for recommendation with causal embedding. In Proceedings of the Web Conference 2021 (pp. 2980-2991). Disentangled Causal Embeddings WWW 2021 Python/Torch

Exposure Bias

Name Paper Causal Inference Method Venue Year Code
IPS Schnabel, T., Swaminathan, A., Singh, A., Chandak, N., & Joachims, T. (2016, June). Recommendations as treatments: Debiasing learning and evaluation. In international conference on machine learning (pp. 1670-1679). PMLR. IPW ICML 2016 Python
Rel-MF Saito, Y., Yaginuma, S., Nishino, Y., Sakata, H., & Nakata, K. (2020, January). Unbiased recommender learning from missing-not-at-random implicit feedback. In Proceedings of the 13th International Conference on Web Search and Data Mining (pp. 501-509). IPW WSDM 2020 Python/TF
Multi-IPW/DR Zhang, W., Bao, W., Liu, X. Y., Yang, K., Lin, Q., Wen, H., & Ramezani, R. (2020, April). Large-scale causal approaches to debiasing post-click conversion rate estimation with multi-task learning. In Proceedings of The Web Conference 2020 (pp. 2775-2781). IPW, DR WWW 2020 N/A
MF-DR-JL Wang, X., Zhang, R., Sun, Y., & Qi, J. (2019, May). Doubly robust joint learning for recommendation on data missing not at random. In International Conference on Machine Learning (pp. 6638-6647). PMLR. DR ICML 2019 N/A
DR Saito, Y. (2020, September). Doubly robust estimator for ranking metrics with post-click conversions. In Fourteenth ACM Conference on Recommender Systems (pp. 92-100). DR RecSys 2020 N/A
MRDR Guo, S., Zou, L., Liu, Y., Ye, W., Cheng, S., Wang, S., ... & Chang, Y. (2021, July). Enhanced doubly robust learning for debiasing post-click conversion rate estimation. In Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval (pp. 275-284). DR SIGIR 2021 Python/TF
LTD Wang, X., Zhang, R., Sun, Y., & Qi, J. (2021, March). Combating selection biases in recommender systems with a few unbiased ratings. In Proceedings of the 14th ACM International Conference on Web Search and Data Mining (pp. 427-435). RCT, DR WSDM 2021 N/A
AutoDebias Chen, J., Dong, H., Qiu, Y., He, X., Xin, X., Chen, L., ... & Yang, K. (2021, July). AutoDebias: Learning to debias for recommendation. In Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval (pp. 21-30). RCT SIGIR 2021 Python/Torch
USR Wang, Z., Shen, S., Wang, Z., Chen, B., Chen, X., & Wen, J. R. (2022, April). Unbiased Sequential Recommendation with Latent Confounders. In Proceedings of the ACM Web Conference 2022 (pp. 2195-2204). IPW WWW 2022 N/A
InvPref Wang, Z., He, Y., Liu, J., Zou, W., Yu, P. S., & Cui, P. (2022, August). Invariant Preference Learning for General Debiasing in Recommendation. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (pp. 1969-1978). Invariant Learning KDD 2022 Python/Torch

Causal Inference-based Recommendation for Addressing Data Missing and Noise

Data Missing

Name Paper RecSys Task Causal Inference Method Venue Year Code
CauseRec Zhang, S., Yao, D., Zhao, Z., Chua, T. S., & Wu, F. (2021, July). Causerec: Counterfactual user sequence synthesis for sequential recommendation. In Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval (pp. 367-377). Sequential Counterfactual SIGIR 2021 N/A
CASR Wang, Z., Zhang, J., Xu, H., Chen, X., Zhang, Y., Zhao, W. X., & Wen, J. R. (2021, July). Counterfactual data-augmented sequential recommendation. In Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval (pp. 347-356). Sequential Counterfactual SIGIR 2021 N/A
CF2 Xiong, K., Ye, W., Chen, X., Zhang, Y., Zhao, W. X., Hu, B., ... & Zhou, J. (2021, October). Counterfactual Review-based Recommendation. In Proceedings of the 30th ACM International Conference on Information & Knowledge Management (pp. 2231-2240). Feature-based Counterfactual CIKM 2021 N/A
ASCKG-CG Mu, S., Li, Y., Zhao, W. X., Wang, J., Ding, B., & Wen, J. R. (2022, July). Alleviating Spurious Correlations in Knowledge-aware Recommendations through Counterfactual Generator. In Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval (pp. 1401-1411). KG-based Counterfactual SIGIR 2022 Python/Torch
CPR Yang, M., Dai, Q., Dong, Z., Chen, X., He, X., & Wang, J. (2021, October). Top-N Recommendation with Counterfactual User Preference Simulation. In Proceedings of the 30th ACM International Conference on Information & Knowledge Management (pp. 2342-2351). Collaborative Filtering SCM, Counterfactual CIKM 2021 N/A
ULO Sato, M., Singh, J., Takemori, S., Sonoda, T., Zhang, Q., & Ohkuma, T. (2019, September). Uplift-based evaluation and optimization of recommenders. In Proceedings of the 13th ACM Conference on Recommender Systems (pp. 296-304). Collaborative Filtering Uplift, IPW RecSys 2019 N/A
DLCE Sato, M., Takemori, S., Singh, J., & Ohkuma, T. (2020, September). Unbiased learning for the causal effect of recommendation. In Fourteenth ACM Conference on Recommender Systems (pp. 378-387). Collaborative Filtering IPW RecSys 2020 N/A
CBI Sato, M. (2021, September). Online Evaluation Methods for the Causal Effect of Recommendations. In Fifteenth ACM Conference on Recommender Systems (pp. 96-101). Collaborative Filtering Interleaving, IPW RecSys 2021 Python/R
CausCF Xie, X., Liu, Z., Wu, S., Sun, F., Liu, C., Chen, J., ... & Ding, B. (2021, October). CausCF: Causal Collaborative Filtering for Recommendation Effect Estimation. In Proceedings of the 30th ACM International Conference on Information & Knowledge Management (pp. 4253-4263). Collaborative Filtering Uplift, RDD CIKM 2021 N/A
DRIB Xiao, T., & Wang, S. (2022, February). Towards unbiased and robust causal ranking for recommender systems. In Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining (pp. 1158-1167). Collaborative Filtering Doubly-Robust, IPW WSDM 2022 N/A
COR Wang, W., Lin, X., Feng, F., He, X., Lin, M., & Chua, T. S. (2022, April). Causal Representation Learning for Out-of-Distribution Recommendation. In Proceedings of the ACM Web Conference 2022 (pp. 3562-3571). CTR Counterfactual WWW 2022 N/A
CausPref He, Y., Wang, Z., Cui, P., Zou, H., Zhang, Y., Cui, Q., & Jiang, Y. (2022, April). CausPref: Causal Preference Learning for Out-of-Distribution Recommendation. In Proceedings of the ACM Web Conference 2022 (pp. 410-421). Collaborative Filtering Invariant Learning WWW 2022 Python/Torch

Data Noise

Name Paper RecSys Task Causal Inference Method Venue Year Code
CBDF Zhang, X., Jia, H., Su, H., Wang, W., Xu, J., & Wen, J. R. (2021, July). Counterfactual reward modification for streaming recommendation with delayed feedback. In Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval (pp. 41-50). Streaming Importance Sampling SIGIR 2021 Python

Beyond-accuracy RecSys with Causal Inference

Explainability

Name Paper RecSys Task Causal Inference Method Venue Year Code
PGPR Xian, Y., Fu, Z., Muthukrishnan, S., De Melo, G., & Zhang, Y. (2019, July). Reinforcement knowledge graph reasoning for explainable recommendation. In Proceedings of the 42nd international ACM SIGIR conference on research and development in information retrieval (pp. 285-294). KG-enhanced Causal Discovery SIGIR 2019 Python/Torch
CountER Tan, J., Xu, S., Ge, Y., Li, Y., Chen, X., & Zhang, Y. (2021, October). Counterfactual explainable recommendation. In Proceedings of the 30th ACM International Conference on Information & Knowledge Management (pp. 1784-1793). Collaborative Filtering Counterfactual, Causal Discovery CIKM 2021 Python/Torch
MCT Tran, H. X., Le, T. D., Li, J., Liu, L., Liu, J., Zhao, Y., & Waters, T. (2021, August). Recommending the Most Effective Intervention to Improve Employment for Job Seekers with Disability. In Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining (pp. 3616-3626). CTR Couterfactual KDD 2021 Python/R
CLSR Zheng, Y., Gao, C., Chang, J., Niu, Y., Song, Y., Jin, D., & Li, Y. (2022, April). Disentangling Long and Short-Term Interests for Recommendation. In Proceedings of the ACM Web Conference 2022 (pp. 2256-2267). Sequential Disentangled Embedding WWW 2022 Python/TF
IV4Rec Si, Z., Han, X., Zhang, X., Xu, J., Yin, Y., Song, Y., & Wen, J. R. (2022, April). A Model-Agnostic Causal Learning Framework for Recommendation using Search Data. In Proceedings of the ACM Web Conference 2022 (pp. 224-233). CTR Decomposed Embeddings WWW 2022 Python/Torch

Diversity

Name Paper RecSys Task Causal Inference Method Venue Year Code
DecRS Wang, W., Feng, F., He, X., Wang, X., & Chua, T. S. (2021, August). Deconfounded recommendation for alleviating bias amplification. In Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining (pp. 1717-1725). Collaborative Filtering Backdoor Adjustment KDD 2021 Python/Torch
UCRS Wang, W., Feng, F., Nie, L., & Chua, T. S. (2022). User-controllable Recommendation Against Filter Bubbles. arXiv preprint arXiv:2204.13844. CTR Counterfactual SIGIR 2022 Python/Torch

Fairness

Name Paper RecSys Task Causal Inference Method Venue Year Code
CBDF Zhang, X., Jia, H., Su, H., Wang, W., Xu, J., & Wen, J. R. (2021, July). Counterfactual reward modification for streaming recommendation with delayed feedback. In Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval (pp. 41-50). Streaming Importance Sampling SIGIR 2021 Python