Reformulating CTR Prediction: Learning Invariant Feature Interactions for Recommendation-Reference-Cited by-同舟云学术

Reformulating CTR Prediction: Learning Invariant Feature Interactions for Recommendation

Published:2023-07-18 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval
language:
Short-container-title:

Author:

Zhang Yang¹^ORCID,Shi Tianhao¹^ORCID,Feng Fuli¹^ORCID,Wang Wenjie²^ORCID,Wang Dingxian³^ORCID,He Xiangnan¹^ORCID,Zhang Yongdong¹^ORCID

Affiliation:

1. University of Science and Technology of China, Hefei, China

2. National University of Singapore, Singapore, Singapore

3. Etsy Inc., Brooklyn, USA

Funder

National Key Research and Development Program of China

National Natural Science Foundation of China

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3539618.3591755

Reference46 articles.

1. Kartik Ahuja , Karthikeyan Shanmugam , Kush Varshney , and Amit Dhurandhar . 2020 . Invariant Risk Minimization Games. In International Conference on Machine Learning. PMLR, 145--155 . Kartik Ahuja, Karthikeyan Shanmugam, Kush Varshney, and Amit Dhurandhar. 2020. Invariant Risk Minimization Games. In International Conference on Machine Learning. PMLR, 145--155.

2. Martin Arjovsky , Léon Bottou , Ishaan Gulrajani , and David Lopez-Paz . 2019. Invariant risk minimization. arXiv preprint arXiv:1907.02893 ( 2019 ). Martin Arjovsky, Léon Bottou, Ishaan Gulrajani, and David Lopez-Paz. 2019. Invariant risk minimization. arXiv preprint arXiv:1907.02893 (2019).

3. Jun-Hyun Bae , Inchul Choi , and Minho Lee . 2021. Meta-Learned Invariant Risk Minimization. arXiv preprint arXiv:2103.12947 ( 2021 ). Jun-Hyun Bae, Inchul Choi, and Minho Lee. 2021. Meta-Learned Invariant Risk Minimization. arXiv preprint arXiv:2103.12947 (2021).

4. CAN

5. Elliot Creager , Jörn-Henrik Jacobsen , and Richard Zemel . 2021 . Environment Inference for Invariant Learning. In International Conference on Machine Learning. 2189--2200 . Elliot Creager, Jörn-Henrik Jacobsen, and Richard Zemel. 2021. Environment Inference for Invariant Learning. In International Conference on Machine Learning. 2189--2200.

Cited by 4 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. GradCraft: Elevating Multi-task Recommendations through Holistic Gradient Crafting;Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining;2024-08-24

2. FedUD: Exploiting Unaligned Data for Cross-Platform Federated Click-Through Rate Prediction;Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval;2024-07-10

3. TALLRec: An Effective and Efficient Tuning Framework to Align Large Language Model with Recommendation;Proceedings of the 17th ACM Conference on Recommender Systems;2023-09-14

4. Towards Trustworthy Recommender System: A Faithful and Responsible Recommendation Perspective;Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval;2023-07-18