Výsledky vyhľadávania - convolutional network recommendation algorithm

Research on cross‐hierarchical graph network recommendation algorithm based on random walk and convolutional neural network Autor Huang, Meng, Sun, Guohui

“… Therefore, this article proposes a heterogeneous network recommendation algorithm based on random walk and convolutional neural network (RW‐CNN…”
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Multi-Behavior Enhanced Heterogeneous Graph Convolutional Networks Recommendation Algorithm based on Feature-Interaction Autor Li, Yang, Zhao, Fangtao, Chen, Zheng, Fu, Yingxun, Ma, Li

“…-item-feature, and propose the attention interaction graph convolutional neural network recommendation algorithm (ATGCN…”
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Research on Tourism Route Recommendation Strategy Based on Convolutional Neural Network and Collaborative Filtering Algorithm Autor He, Shan

“… It is difficult for passengers to choose the route that suits them from the numerous routes. Given the above problems, this study proposes a travel route recommendation algorithm that combines a convolutional neural network and collaborative filtering…”
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Convolutional neural network and recommendation algorithm for the new model of college music education Autor Bai, Hua

“… In this paper, we design a convolutional neural network music recommendation system, including a user modeling module, audio feature extraction module recommendation algorithm module, etc…”
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A novel Knowledge Graph recommendation algorithm based on Graph Convolutional Network Autor Guo, Hui, Yang, Chengyong, Zhou, Liqing, Wei, Shiwei

“… Therefore, we propose a novel Knowledge Graph (KG) recommendation algorithms. Due to the availability of interaction data across numerous events, KGs also exhibit dynamics over time…”
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Convolutional Neural Network based Recommendation Algorithm of Instructional Resources Mechanism for Online Music Education Autor Zhao, Yu, Lin, Bin

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Optimized multi‐label convolutional neural network using modified genetic algorithm for popularity based personalized news recommendation system Autor Manoharan, Saravanapriya, Senthilkumar, Radha, Jayakumar, Saktheeswaran

“…‐label convolutional neural network (MLCNN). The efficiency of MLCNN model highly depends on its architecture through the tuning of its hyperparameters…”
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Research and Analysis of Recommendation Algorithm Based on Convolutional Neural Network Autor Ding, Yuhang, Jiang, Wenrong

“…) algorithm which is getting more and more attention, organizes the research progress of recommendation system based on Convolutional Neural Networks (CNN…”
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A multitask recommendation algorithm based on DeepFM and Graph Convolutional Network Autor Chen, Liqiong, Bi, Xiaoyu, Fan, Guoqing, Sun, Huaiying

“… Thus, a multitask recommendation algorithm based on DeepFM and graph convolutional network (DeepFM_GCN) is proposed…”
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A multi-intent-aware recommendation algorithm based on interactive graph convolutional networks Autor Zhang, Junsan, Gao, Hui, Xiao, Sen, Zhu, Jie, Wang, Jian

“… To address the aforementioned issues, we propose a recommendation model called multi-intent-aware interactive graph convolutional network (Multi-IAIGCN…”
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Recommendation of Learning Resource Based on Knowledge Graph Convolutional Network Autor TANG Zhikang, WU Yuqi, LI Chunying, TANG Yong

“…Aiming at random sampling and the selection of neighborhoods that may lead to unstable recommendation results in existing Knowledge Graph Convolutional Network(KGCN…”
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Retracted: Research on Tourism Route Recommendation Strategy Based on Convolutional Neural Network and Collaborative Filtering Algorithm Autor and Communication Networks, Security

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Deep Learning algorithm, based on convolutional neural networks, for equivalent electrical circuit recommendation for Electrochemical Impedance Spectroscopy Autor Sáez-Pardo, Fermín, Giner-Sanz, Juan José, Pérez-Herranz, Valentín

“…•Convolutional Neural Networks were optimized for the first time for EEC recommendation•The algorithm achieved better recommendation accuracies than previous AI algorithms…”
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Recommendation Algorithm for Multi-Task Learning with Directed Graph Convolutional Networks Autor Yin, Lifeng, Lu, Jianzheng, Zheng, Guanghai, Chen, Huayue, Deng, Wu

“… In order to overcome these problems and improve the accuracy of personalized recommendation algorithms, this paper proposes a recommendation for multi-task learning based on directed graph convolutional network…”
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Research on Graph Network Recommendation Algorithm Based on Random Walk and Convolutional Neural Network Autor Huang, Meng

“…As a general form of describing and modeling complex systems, networks widely exist in different scenes and tasks in various fields of the real world…”
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An attention-based convolutional neural network for recipe recommendation Autor Jia, Nan, Chen, Jie, Wang, Rongzheng

“… In this paper, we propose an attention-based convolutional neural network for recipe recommendation…”
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Music Genre Recommendations Based on Spectrogram Analysis Using Convolutional Neural Network Algorithm with RESNET-50 and VGG-16 Architecture Autor Purnama, Nyoman

“… In this study, a spectrogram analysis was developed which will be the input feature for the Convolutional Neural Network…”
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Human resource recommendation algorithm based on convolutional neural network Autor Liu, Jiakun, Li, Haoran, Wan, Li

“… As an important network model of deep learning, convolutional neural network has the characteristics of hierarchical structure, weight sharing, regional local perception, feature…”
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College Student Employment Recommendation Algorithm Based on Convolutional Neural Network Autor Zhang, Liwen, Wei, Liu

“… Accelerating school development plays a vital role. The purpose of this paper is to study the research and design of college students' employment recommendation algorithm based on convolutional neural network…”
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PRAGCN: Personalized Recommendation Algorithm based on Graph Convolutional Network Technology Autor Xie, Zheng, Zhang, Biao

“…The recommendation system aims to provide users with personalized matching services, so as to effectively alleviate the problem of information overload in the era of big data, improve user experience…”
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