Explicit Feature Interaction-aware Uplift Network for Online Marketing-Reference-Cited by-同舟云学术

Explicit Feature Interaction-aware Uplift Network for Online Marketing

Published:2023-08-04 Issue: Volume: Page:
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Container-title:Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
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Author:

Liu Dugang¹^ORCID,Tang Xing²^ORCID,Gao Han²^ORCID,Lyu Fuyuan³^ORCID,He Xiuqiang²^ORCID

Affiliation:

1. Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ), Shenzhen University, Shenzhen, China

2. FiT, Tencent, Shenzhen, China

3. McGill University, Montreal, Canada

Funder

National Natural Science Foundation of China

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3580305.3599820

Reference39 articles.

1. LBCF: A Large-Scale Budget-Constrained Causal Forest Algorithm

2. Javier Albert and Dmitri Goldenberg . 2021. E-commerce promotions personalization via online multiple-choice knapsack with uplift modeling. arXiv preprint arXiv:2108.13298 ( 2021 ). Javier Albert and Dmitri Goldenberg. 2021. E-commerce promotions personalization via online multiple-choice knapsack with uplift modeling. arXiv preprint arXiv:2108.13298 (2021).

3. Recursive partitioning for heterogeneous causal effects

4. Doubly Robust Estimation in Missing Data and Causal Inference Models

5. Uplift Modeling with Generalization Guarantees

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2. Towards Effective and Efficient Multi-valued Treatment Uplift Modeling in Online Marketing;Lecture Notes in Computer Science;2024

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