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A differentially private nonnegative matrix factorization for recommender system
期刊论文
Information Sciences, 2022, 卷号: 592, 页码: 21-35
作者:
Ran, Xun
;
Wang, Yong
;
Zhang, Leo Yu
;
Ma, Jun
收藏
  |  
浏览/下载:29/0
  |  
提交时间:2022/04/21
Benchmarking
Collaborative filtering
Economic and social effects
Information services
Large dataset
Matrix algebra
Matrix factorization
Privacy-preserving techniques
Differential privacies
Factorization methods
Filtering problems
Imputation
Non-negativity
Nonnegative matrix factorization
Objective functions
Personalized information services
Polynomial expression
User data
CF-DAML: Distributed automated machine learning based on collaborative filtering
期刊论文
APPLIED INTELLIGENCE, 2022, 页码: 1-25
作者:
Liu PJ(刘朋杰)
;
Pan FC(潘福成)
;
Zhou XF(周晓锋)
;
Li S(李帅)
;
Jin L(金樑)
收藏
  |  
浏览/下载:14/0
  |  
提交时间:2022/04/14
Automated machine learning
Collaborative filtering
Weighted l(1)-norm
Distributed automated system
Multilayer selective stacked ensemble
Knowledge Graph enhanced Neural Collaborative Filtering with Residual Recurrent Network
期刊论文
NEUROCOMPUTING, 2021, 卷号: 454, 页码: 417-429
作者:
Sang, Lei
;
Xu, Min
;
Qian, Shengsheng
;
Wu, Xindong
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  |  
浏览/下载:82/0
  |  
提交时间:2021/08/15
Recommendation system
Knowledge Graph
Relational Path Embedding
Neural Collaborative Filtering
Residual Recurrent Network
Algorithms of Unconstrained Non-Negative Latent Factor Analysis for Recommender Systems
期刊论文
IEEE TRANSACTIONS ON BIG DATA, 2021, 卷号: 7, 期号: 1, 页码: 227-240
作者:
Luo, Xin
;
Zhou, Mengchu
;
Li, Shuai
;
Wu, Di
;
Liu, Zhigang
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  |  
浏览/下载:25/0
  |  
提交时间:2021/05/17
Data models
Training
Sparse matrices
Recommender systems
Computational modeling
Big Data
Scalability
Non-negative latent factor analysis
non-negativity
latent factor analysis
unconstrained optimization
high-dimensional and sparse matrix
collaborative filtering
recommender system
big data
Latent Factor-Based Recommenders Relying on Extended Stochastic Gradient Descent Algorithms
期刊论文
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS, 2021, 卷号: 51, 期号: 2, 页码: 916-926
作者:
Luo, Xin
;
Wang, Dexian
;
Zhou, MengChu
;
Yuan, Huaqiang
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  |  
浏览/下载:20/0
  |  
提交时间:2021/03/17
Big data
bi-linear
collaborative filtering (CF)
high-dimensional and sparse (HiDS) matrix
industry
latent factor (LF) analysis
missing data
recommender system
An Overview of Recommendation Techniques and Their Applications in Healthcare
期刊论文
IEEE/CAA Journal of Automatica Sinica, 2021, 卷号: 8, 期号: 4, 页码: 701-717
作者:
Wenbin Yue
;
Zidong Wang
;
Jieyu Zhang
;
Xiaohui Liu
收藏
  |  
浏览/下载:20/0
  |  
提交时间:2021/04/09
Collaborative filtering (CF)
content-based recommendation
healthcare
recommendation system (RS)
A mobile services recommendation system fuses implicit and explicit user trust relationships
期刊论文
JOURNAL OF AMBIENT INTELLIGENCE AND SMART ENVIRONMENTS, 2021, 卷号: 13, 期号: 1, 页码: 21-35
作者:
Luo, Pengcheng
;
Zhang, Jilin
;
Wan, Jian
;
Zhao, Nailiang
;
Ren, Zujie
收藏
  |  
浏览/下载:20/0
  |  
提交时间:2021/12/01
Collaborative filtering
data management
data mining
matrix factorization
recommendation system
social network
A hybrid recommendation system with many-objective evolutionary algorithm
期刊论文
EXPERT SYSTEMS WITH APPLICATIONS, 2020, 卷号: 159, 页码: 10
作者:
Cai, Xingjuan
;
Hu, Zhaoming
;
Zhao, Peng
;
Zhang, WenSheng
;
Chen, Jinjun
收藏
  |  
浏览/下载:27/0
  |  
提交时间:2021/01/06
Recommendation systems
Many-objective optimization
Hybrid recommender algorithm
Collaborative filtering
BPF plus plus : A Unified Factorization model for predicting retweet behaviors
期刊论文
INFORMATION SCIENCES, 2020, 卷号: 515, 页码: 218-232
作者:
Wang, Shaoqing
;
Li, Cuipin
;
Wang, Zheng
;
Chen, Hong
;
Zheng, Kai
收藏
  |  
浏览/下载:9/0
  |  
提交时间:2020/12/10
Collaborative filtering
Bayesian Poisson factorization
Probabilistic model
Retweet behaviors
Social network
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
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  |  
浏览/下载:85/0
  |  
提交时间:2019/12/10
collaborative filtering
Bayesian neural network
hybrid recommendation algorithm
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