Peng Wu

Orcid: 0000-0001-7154-8880

Affiliations:
  • Beijing Technology and Business University (BTBU), Beijing, China


According to our database1, Peng Wu authored at least 16 papers between 2022 and 2023.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

Legend:

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Bibliography

2023
Balancing Unobserved Confounding with a Few Unbiased Ratings in Debiased Recommendations.
Proceedings of the ACM Web Conference 2023, 2023

Causal Recommendation: Progresses and Future Directions.
Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval, 2023

Who Should Be Given Incentives? Counterfactual Optimal Treatment Regimes Learning for Recommendation.
Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2023

Propensity Matters: Measuring and Enhancing Balancing for Recommendation.
Proceedings of the International Conference on Machine Learning, 2023

StableDR: Stabilized Doubly Robust Learning for Recommendation on Data Missing Not at Random.
Proceedings of the Eleventh International Conference on Learning Representations, 2023

TDR-CL: Targeted Doubly Robust Collaborative Learning for Debiased Recommendations.
Proceedings of the Eleventh International Conference on Learning Representations, 2023

Multiple Robust Learning for Recommendation.
Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence, 2023

2022
A Generalized Doubly Robust Learning Framework for Debiasing Post-Click Conversion Rate Prediction.
CoRR, 2022

Stabilized Doubly Robust Learning for Recommendation on Data Missing Not at Random.
CoRR, 2022

Doubly Robust Collaborative Targeted Learning for Recommendation on Data Missing Not at Random.
CoRR, 2022

A Semi-Synthetic Dataset Generation Framework for Causal Inference in Recommender Systems.
CoRR, 2022

Causal Analysis Framework for Recommendation.
CoRR, 2022


Addressing Unmeasured Confounder for Recommendation with Sensitivity Analysis.
Proceedings of the KDD '22: The 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Washington, DC, USA, August 14, 2022

A Generalized Doubly Robust Learning Framework for Debiasing Post-Click Conversion Rate Prediction.
Proceedings of the KDD '22: The 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Washington, DC, USA, August 14, 2022

On the Opportunity of Causal Learning in Recommendation Systems: Foundation, Estimation, Prediction and Challenges.
Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, 2022


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