AdnFM: An Attentive DenseNet based Factorization Machine for Click-Through-Rate Prediction
Author:
Affiliation:
1. Tencent, China
2. Tencent Lightspeed&Quantum Studios, Tencent, China
Publisher
ACM
Link
https://dl.acm.org/doi/pdf/10.1145/3512850.3512852
Reference29 articles.
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2. Xiaoshuang Chen Yin Zheng Jiaxing Wang Wenye Ma and Junzhou Huang. 2019. RaFM: Rank-Aware Factorization Machines. In ICML. Xiaoshuang Chen Yin Zheng Jiaxing Wang Wenye Ma and Junzhou Huang. 2019. RaFM: Rank-Aware Factorization Machines. In ICML.
3. Heng-Tze Cheng Levent Koc Jeremiah Harmsen Tal Shaked Tushar Chandra Hrishi Aradhye Glen Anderson Greg Corrado Wei Chai Mustafa Ispir 2016. Wide & deep learning for recommender systems. In RecSys. Heng-Tze Cheng Levent Koc Jeremiah Harmsen Tal Shaked Tushar Chandra Hrishi Aradhye Glen Anderson Greg Corrado Wei Chai Mustafa Ispir 2016. Wide & deep learning for recommender systems. In RecSys.
4. Paul Covington Jay Adams and Emre Sargin. 2016. Deep neural networks for youtube recommendations. In RecSys. Paul Covington Jay Adams and Emre Sargin. 2016. Deep neural networks for youtube recommendations. In RecSys.
5. Magdalini Eirinaki Jerry Gao Iraklis Varlamis and Konstantinos Tserpes. 2018. Recommender systems for large-scale social networks: A review of challenges and solutions. Magdalini Eirinaki Jerry Gao Iraklis Varlamis and Konstantinos Tserpes. 2018. Recommender systems for large-scale social networks: A review of challenges and solutions.
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