FAN: Fatigue-Aware Network for Click-Through Rate Prediction in E-commerce Recommendation-Reference-Cited by-同舟云学术

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FAN: Fatigue-Aware Network for Click-Through Rate Prediction in E-commerce Recommendation

Published:2023 Issue: Volume: Page:502-514
ISSN:0302-9743
Container-title:Database Systems for Advanced Applications
language:
Short-container-title:

Author:

Li Ming,Liu Naiyin,Pan Xiaofeng,Huang Yang,Li Ningning,Su Yingmin,Mao Chengjun,Cao Bo

Publisher

Springer Nature Switzerland

Link

https://link.springer.com/content/pdf/10.1007/978-3-031-30678-5_37

Reference26 articles.

1. Ai, Z., Wang, S., Jia, S., Guo, S.: Core interests focused self-attention for sequential recommendation. In: Database Systems for Advanced Applications, vol. 13246. pp. 306–314. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-00126-0_23

2. Auer, P.: Using confidence bounds for exploitation-exploration trade-offs. J. Mach. Learn. Res. 3, 397–422 (2002)

3. Bian, Z., et al.: Denoising user-aware memory network for recommendation. In: Fifteenth ACM Conference on Recommender Systems, pp. 400–410 (2021)

4. Cao, J., Sun, W., Shen, Z.J.M., Ettl, M.: Fatigue-aware bandits for dependent click models. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 3341–3348 (2020)

5. Covington, P., Adams, J., Sargin, E.: Deep neural networks for youtube recommendations. In: Proceedings of the 10th ACM Conference on Recommender Systems, pp. 191–198 (2016)

Cited by 2 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. Avoiding Decision Fatigue with AI-Assisted Decision-Making;Proceedings of the 32nd ACM Conference on User Modeling, Adaptation and Personalization;2024-06-22

2. DPAN: Dynamic Preference-based and Attribute-aware Network for Relevant Recommendations;Proceedings of the 32nd ACM International Conference on Information and Knowledge Management;2023-10-21

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