Click-through conversion rate prediction model of book e-commerce platform based on feature combination and representation-Reference-Cited by-同舟云学术

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Click-through conversion rate prediction model of book e-commerce platform based on feature combination and representation

Published:2024-03 Issue: Volume:238 Page:122276
ISSN:0957-4174
Container-title:Expert Systems with Applications
language:en
Short-container-title:Expert Systems with Applications

Author:

Wei Shihong,Yang Zhou,Zhang Jian,Zeng Yang,Li Qian,Xiao Yunpeng^ORCID

Publisher

Elsevier BV

Subject

Artificial Intelligence,Computer Science Applications,General Engineering

Reference41 articles.

1. Bian, W., Wu, K., Ren, L., Pi, Q., Zhang, Y., Xiao, C., et al. (2022). CAN: feature co-action network for click-through rate prediction. In Proceedings of the fifteenth ACM international conference on web search and data mining (pp. 57–65).

2. Wide & deep learning for recommender systems;Cheng,2016

3. Cheng, W., Shen, Y., & Huang, L. (2020). Adaptive factorization network: Learning adaptive-order feature interactions. In Proceedings of the AAAI conference on artificial intelligence, vol. 34 (pp. 3609–3616).

4. Covington, P., Adams, J., & Sargin, E. (2016). Deep neural networks for youtube recommendations. In Proceedings of the 10th ACM conference on recommender systems (pp. 191–198).

5. Personalized recommendation system based on collaborative filtering for IoT scenarios;Cui;IEEE Transactions on Services Computing,2020

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