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Follow the Title Then Read the Article: Click-Guide Network for Dwell Time Prediction 期刊论文
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, 2021, 卷号: 33, 期号: 7, 页码: 2903-2913
作者:  Chen, Jingwu;  Zhuang, Fuzhen;  Wang, Tianxin;  Lin, Leyu;  Xia, Feng;  Du, Lihuan;  He, Qing
收藏  |  浏览/下载：42/0  |  提交时间：2021/12/01
Task analysis  Predictive models  Lifting equipment  Mobile applications  Collaboration  User experience  Article recommendation  dwell time prediction  click-guide network  
Adaptive Deep Modeling of Users and Items Using Side Information for Recommendation 期刊论文
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2020, 卷号: 31, 期号: 3, 页码: 737-748
作者:  Han, Jiayu;  Zheng, Lei;  Xu, Yuanbo;  Zhang, Bangzuo;  Zhuang, Fuzhen;  Yu, Philip S.;  Zuo, Wanli
收藏  |  浏览/下载：66/0  |  提交时间：2020/12/10
Adaptation models  Recommender systems  Feature extraction  Computational modeling  Predictive models  Task analysis  Adaptive systems  Adaptive user preference model  attention factor  convolutional neural network (CNN)  recommendation system  
Bayesian dual neural networks for recommendation 期刊论文
FRONTIERS OF COMPUTER SCIENCE, 2019, 卷号: 13, 期号: 6, 页码: 1255-1265
作者:  He, Jia;  Zhuang, Fuzhen;  Liu, Yanchi;  He, Qing;  Lin, Fen
收藏  |  浏览/下载：303/0  |  提交时间：2019/12/10
collaborative filtering  Bayesian neural network  hybrid recommendation algorithm  
Neural variational collaborative filtering with side information for top-K recommendation 期刊论文
INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS, 2019, 卷号: 10, 期号: 11, 页码: 3273-3284
作者:  Deng, Xiaoyi;  Zhuang, Fuzhen;  Zhu, Zhiguo
收藏  |  浏览/下载：42/0  |  提交时间：2020/12/10
Neural collaborative filtering  Variational autoencoder  Top-K recommendation  Side information  Implicit feedback  
Representation learning via Dual-Autoencoder for recommendation 期刊论文
NEURAL NETWORKS, 2017, 卷号: 90, 页码: 83-89
作者:  Zhuang, Fuzhen;  Zhang, Zhiqiang;  Qian, Mingda;  Shi, Chuan;  Xie, Xing;  He, Qing
收藏  |  浏览/下载：55/0  |  提交时间：2019/12/12
Matrix factorization  Dual-Autoencoder  Recommendation  Representation learning