Continual Transfer Learning for Cross-Domain Click-Through Rate Prediction at Taobao-Reference-Cited by-同舟云学术

Continual Transfer Learning for Cross-Domain Click-Through Rate Prediction at Taobao

Published:2023-04-30 Issue: Volume: Page:
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Container-title:Companion Proceedings of the ACM Web Conference 2023
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Author:

Liu Lixin¹^ORCID,Wang Yanling²^ORCID,Wang Tianming¹^ORCID,Guan Dong¹^ORCID,Wu Jiawei¹^ORCID,Chen Jingxu¹^ORCID,Xiao Rong¹^ORCID,Zhu Wenxiang¹^ORCID,Fang Fei¹^ORCID

Affiliation:

1. Alibaba Group, China

2. Renmin University of China, China

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3543873.3584625

Reference31 articles.

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2. Qiwei Chen, Yue Xu, Changhua Pei, Shanshan Lv, Tao Zhuang, and Junfeng Ge. 2022. Efficient Long Sequential User Data Modeling for Click-Through Rate Prediction. In 4th Workshop on Deep Learning Practice and Theory for High-Dimensional Sparse and Imbalanced Data (DLP-KDD).

3. Y. N. Dauphin, A. Fan, M. Auli, and D. Grangier. 2016. Language Modeling with Gated Convolutional Networks. In Proceedings of the 34th International Conference on Machine Learning (ICML).

4. Chen et al.2020. Behavior sequence transformer for e-commerce recommendation in alibaba. In International Workshop on Deep Learning Practice for High-Dimensional Sparse Data (DLP-KDD).

5. Heng-Tze Cheng et al.2016. Wide & Deep Learning for Recommender Systems. In Proceedings of the 1st Workshop on Deep Learning for Recommender Systems (DLRS). arXiv:1606.07792

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