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检索条件"主题词=Semi-autoencoder"
7 条 记 录,以下是1-10 订阅
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Differentially private recommender framework with Dual semi-autoencoder
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EXPERT SYSTEMS WITH APPLICATIONS 2024年 260卷
作者: Deng, Yang Zhou, Wang Ul Haq, Amin Ahmad, Sultan Tabassum, Alia Xihua Univ Sch Comp & Software Engn Chengdu Peoples R China Univ Elect Sci & Technol China Sch Comp Sci & Engn Chengdu Peoples R China Prince Sattam Bin Abdulaziz Univ Coll Comp Engn & Sci Dept Comp Sci Alkharj Saudi Arabia Mohi Ud Din Islamic Univ Azad Kashmir Pakistan
To provide much better recommendation service, traditional recommender systems collect a large amount of user information, which, if obtained and analyzed maliciously, can cause incalculable damage to users. Therefore... 详细信息
来源: 评论
Hybrid Collaborative Recommendation via semi-autoencoder  24th
Hybrid Collaborative Recommendation via Semi-AutoEncoder
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24th International Conference on Neural Information Processing (ICONIP)
作者: Zhang, Shuai Yao, Lina Xu, Xiwei Wang, Sen Zhu, Liming Univ New South Wales Sch Comp Sci & Engn Kensington NSW Australia CSIRO Data61 Sydney NSW Australia Griffith Univ Sch Informat & Commun Technol Nathan Qld Australia
In this paper, we present a novel structure, semi-autoencoder, based on autoencoder. We generalize it into a hybrid collaborative filtering model for rating prediction as well as personalized top-n recommendations. Ex... 详细信息
来源: 评论
Deep Learning Aided SID in Near-Field Power Internet of Things Networks With Hybrid Recommendation Algorithm
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COMPUTATIONAL INTELLIGENCE 2025年 第1期41卷
作者: Chen, Chuangang Wu, Qiang Wang, Hangao Chen, Jing Elect Power Res Inst Hainan Power Grid Co Ltd Haikou Peoples R China Joint Lab Smart Grid & Isl Microgrid Haikou Peoples R China
In the realm of power Internet of Things (IoT) networks, secure inspection detection (SID) is paramount for maintaining system integrity and security. This paper presents a novel framework that leverages deep learning... 详细信息
来源: 评论
Deep matrix factorization via feature subspace transfer for recommendation system
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COMPLEX & INTELLIGENT SYSTEMS 2024年 第4期10卷 4939-4954页
作者: Wang, Weichen Wang, Jing Huaqiao Univ Sch Comp Sci & Technol 668 Jimei Ave Xiamen 361021 Fujian Peoples R China
The sparsity problem remains a significant bottleneck for recommendation systems. In recent years, deep matrix factorization has shown promising results in mitigating this issue. Furthermore, many works have improved ... 详细信息
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Sparse semi-autoencoders to solve the vanishing information problem in multi-layered neural networks
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APPLIED INTELLIGENCE 2019年 第7期49卷 2522-2545页
作者: Kamimura, Ryotaro Takeuchi, Haruhiko Tokai Univ IT Educ Ctr 4-1-1 Kitakaname Hiratsuka Kanagawa 2591292 Japan Natl Inst Adv Ind Sci & Technol Human Informat Res Inst 1-1-1 Higashi Tsukuba Ibaraki 3058566 Japan
The present paper aims to propose a new neural network called sparse semi-autoencoder to overcome the vanishing information problem inherent to multi-layered neural networks. The vanishing information problem represen... 详细信息
来源: 评论
Hybrid Collaborative Recommendation via Dual-autoencoder
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IEEE ACCESS 2020年 8卷 46030-46040页
作者: Dong, Bingbing Zhu, Yi Li, Lei Wu, Xindong Hefei Univ Technol Key Lab Knowledge Engn Big Data Minist Educ Hefei 230009 Peoples R China Hefei Univ Technol Sch Comp Sci & Informat Engn Hefei 230009 Peoples R China Hefei Univ Technol Inst Big Knowledge Sci Hefei 230009 Peoples R China Yangzhou Univ Sch Informat Engn Yangzhou 225009 Jiangsu Peoples R China Mininglamp Acad Sci Mininglamp Technol Beijing 100084 Peoples R China
With the rapid increase of internet information, personalized recommendation systems are an effective way to alleviate the information overload problem, which has attracted extensive attention in recent years. The tra... 详细信息
来源: 评论
Representation learning with collaborative autoencoder for personalized recommendation
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EXPERT SYSTEMS WITH APPLICATIONS 2021年 186卷 115825-115825页
作者: Zhu, Yi Wu, Xindong Qiang, Jipeng Yuan, Yunhao Li, Yun Yangzhou Univ Sch Informat Engn Yangzhou Jiangsu Peoples R China Hefei Univ Technol Minist Educ Key Lab Knowledge Engn Big Data Hefei Peoples R China Hefei Univ Technol Sch Comp Sci & Informat Engn Hefei Peoples R China Mininglamp Acad Sci Mininglamp Technol Beijing Peoples R China
In the past decades, recommendation systems have provided lots of valuable personalized suggestions for the users to address the problem of information over-loaded. Collaborative Filtering (CF) is one of the most comm... 详细信息
来源: 评论