Warming Up Cold-Start CTR Prediction by Learning Item-Specific Feature Interactions-Reference-Cited by-同舟云学术

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Warming Up Cold-Start CTR Prediction by Learning Item-Specific Feature Interactions

Published:2024-08-24 Issue: Volume:53 Page:3233-3244
ISSN:
Container-title:Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
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Author:

Wang Yaqing¹^ORCID,Piao Hongming²^ORCID,Dong Daxiang³^ORCID,Yao Quanming⁴^ORCID,Zhou Jingbo¹^ORCID

Affiliation:

1. Baidu Research, Baidu Inc., Beijing, China

2. Department of Computer Science, City University of Hong Kong, Hong Kong, Hong Kong

3. Baidu AI Cloud, Baidu Inc., Beijing, China

4. Department of Electronic Engineering, Tsinghua University, Beijing, China

Funder

National Natural Science Foundation of China

National Key Research and Development Program of China

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3637528.3671784

Reference46 articles.

1. Feynman: Federated Learning-Based Advertising for Ecosystems-Oriented Mobile Apps Recommendation

2. Mathieu Blondel Akinori Fujino Naonori Ueda and Masakazu Ishihata. 2016. Higher-order factorization machines. In Advances in Neural Information Processing Systems. 3351--3359.

3. Simple and Scalable Response Prediction for Display Advertising

4. Generative Adversarial Framework for Cold-Start Item Recommendation

5. Wide & Deep Learning for Recommender Systems

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