Suchergebnisse - convolutional network recommendation algorithm

基于特征融合轻量图卷积网络的软件漏洞推荐算法.

Alternate Title: Software Vulnerability Recommendation Algorithm Based on Feature Fusion Lightweight Graph Convolutional Networks.

Autoren: 韩坚强 魏嘉银 卢友军

Quelle: Journal of Hubei Minzu University (Natural Science Edition). Dec2024, Vol. 42 Issue 4, p494-499. 6p.

A novel Knowledge Graph recommendation algorithm based on Graph Convolutional Network.

Autoren: Guo, Hui Yang, Chengyong Zhou, Liqing et al.

Quelle: Connection Science. Dec2024, Vol. 36 Issue 1, p1-23. 23p.

Schlagwörter: *KNOWLEDGE graphs, *GRAPH algorithms, *RECOMMENDER systems, *MULTISENSOR data fusion, *ALGORITHMS

Contrastive Learning‑based Simplified Graph Convolutional Network Recommendation

Autoren: YU Yuchen WU Siqi ZHAO Qinghua et al.

Quelle: Taiyuan Ligong Daxue xuebao, Vol 56, Iss 3, Pp 485-494 (2025)

Schlagwörter: Technology, Chemical engineering, contrastive learning, graph convolutional network, self-supervised learning, TA401-492, TP155-156, long-tail item, Materials of engineering and construction. Mechanics of materials

Zugangs-URL: https://doaj.org/article/dc1f9bd0b0454791b2dff5a8ff8c453f

A multi-intent-aware recommendation algorithm based on interactive graph convolutional networks

Autoren: Junsan Zhang Hui Gao Sen Xiao et al.

Quelle: Complex & Intelligent Systems, Vol 10, Iss 3, Pp 4493-4506 (2024)

Schlagwörter: Recommender system, Collaborative filtering, Graph convolutional networks, Attention mechanism, Electronic computers. Computer science, QA75.5-76.95, Information technology, T58.5-58.64

Dateibeschreibung: electronic resource

Relation: https://doaj.org/toc/2199-4536; https://doaj.org/toc/2198-6053

Zugangs-URL: https://doaj.org/article/6bc083acc3c849cfb32d05fcbc890655

Recommendation of Learning Resource Based on Knowledge Graph Convolutional Network

Autoren: TANG Zhikang, WU Yuqi, LI Chunying, TANG Yong

Quelle: Jisuanji gongcheng, Vol 50, Iss 9, Pp 153-160 (2024)

Schlagwörter: knowledge graph(kg), graph convolutional network(gcn), graph sampling, recommendation algorithm, learning resource, Computer engineering. Computer hardware, TK7885-7895, Computer software, QA76.75-76.765

Dateibeschreibung: electronic resource

Relation: https://www.ecice06.com/fileup/1000-3428/PDF/20240915.pdf; https://doaj.org/toc/1000-3428

Zugangs-URL: https://doaj.org/article/029cdc2f7b2e48099f021996ba677679

Light Graph Convolutional Recommendation Algorithm Based on Hybrid Spreading

Autoren: Yaowei Duan Liang Zhang Xu Lu et al.

Quelle: Applied Sciences, Vol 15, Iss 4, p 1898 (2025)

Schlagwörter: recommendation system, spreading-based recommendation, graph convolutional network, Technology, Engineering (General). Civil engineering (General), TA1-2040, Biology (General), QH301-705.5, Physics, QC1-999, Chemistry, QD1-999

Dateibeschreibung: electronic resource

Relation: https://www.mdpi.com/2076-3417/15/4/1898; https://doaj.org/toc/2076-3417

Zugangs-URL: https://doaj.org/article/336b1cdd80d04577b43788fd3523a598

Recommendation Algorithm for Multi-Task Learning with Directed Graph Convolutional Networks

Autoren: Lifeng Yin Jianzheng Lu Guanghai Zheng et al.

Quelle: Applied Sciences, Vol 12, Iss 18, p 8956 (2022)

Schlagwörter: recommendation algorithm, directed graph convolutional network, soft attention mechanism, multi-task learning, knowledge graph embedding, Technology, Engineering (General). Civil engineering (General), TA1-2040, Biology (General), QH301-705.5, Physics, QC1-999, Chemistry, QD1-999

Dateibeschreibung: electronic resource

Relation: https://www.mdpi.com/2076-3417/12/18/8956; https://doaj.org/toc/2076-3417

Zugangs-URL: https://doaj.org/article/feefeb47396741648ace86a1a35fc0b0

融合用户社交关系的自适应图卷积推荐算法.

Alternate Title: Adaptive graph convolutional recommendation algorithm integrating user social relationships.

Autoren: 王 光 尹 凯

Quelle: Application Research of Computers / Jisuanji Yingyong Yanjiu. Feb2024, Vol. 41 Issue 2, p482-487. 6p.

Schlagwörter: *CONVOLUTIONAL neural networks, *RECOMMENDER systems

Research on Knowledge Concept Recommendation Algorithm With Spatial-Temporal Information

Autoren: Zhaoyu Shou Yixin Chen Huibing Zhang et al.

Quelle: IEEE Access, Vol 12, Pp 81527-81540 (2024)

Schlagwörter: Knowledge concept recommendation, graph convolutional networks, recurrent neural networks, online learning, Electrical engineering. Electronics. Nuclear engineering, TK1-9971

Dateibeschreibung: electronic resource

Relation: https://ieeexplore.ieee.org/document/10552755/; https://doaj.org/toc/2169-3536

Zugangs-URL: https://doaj.org/article/eea8aa09e634479f8d4ad086c903f4a1

Temporal-Guided Knowledge Graph-Enhanced Graph Convolutional Network for Personalized Movie Recommendation Systems.

Autoren: Chen, Chin-Yi Huang, Jih-Jeng

Quelle: Future Internet. Oct2023, Vol. 15 Issue 10, p323. 13p.

Schlagwörter: *Recommender systems, *Machine learning, Knowledge graphs, Matrix decomposition, Graph algorithms

Feature-enhanced Recommendation Algorithm Based on Bipartite Graph Contrastive Learning

Autoren: YU Peng, YANG Jiaqi, CHEN Xinran, HE Chaobo

Quelle: Jisuanji gongcheng, Vol 51, Iss 7, Pp 100-110 (2025)

Schlagwörter: deep learning, recommendation algorithm, bipartite graph, contrastive learning (cl), graph convolution network (gcn), Computer engineering. Computer hardware, TK7885-7895, Computer software, QA76.75-76.765

Dateibeschreibung: electronic resource

Relation: https://www.ecice06.com/fileup/1000-3428/PDF/jsjgc-51-7-100.pdf; https://doaj.org/toc/1000-3428

Zugangs-URL: https://doaj.org/article/d766b29001024378b6e569ff45c97e2c

基于距离策略的知识图谱图卷积网络推荐算法. (Chinese)

Alternate Title: Knowledge Graph Convolutional Network Recommendation Algorithm Based on Distance Strategy. (English)

Autoren: 邢长征 刘义海 郭亚兰 et al.

Quelle: Journal of Computer Engineering & Applications; Nov2023, Vol. 59 Issue 21, p102-111, 10p

Schlagwörter: CONVOLUTIONAL neural networks, KNOWLEDGE graphs, COMPLETE graphs, DATA mining, KNOWLEDGE representation (Information theory), GRAPH algorithms

Dual-Channel Graph Collaborative Filtering Recommendation Algorithm

Autoren: MIAO Yuxin, SONG Chunhua, NIU Baoning, KANG Ruixue

Quelle: Jisuanji gongcheng, Vol 48, Iss 8, Pp 121-128 (2022)

Schlagwörter: recommendation algorithm, deep learning, collaborative filtering, graph convolutional network(gcn), vector embedding, Computer engineering. Computer hardware, TK7885-7895, Computer software, QA76.75-76.765

Dateibeschreibung: electronic resource

Relation: https://www.ecice06.com/fileup/1000-3428/PDF/20220815.pdf; https://doaj.org/toc/1000-3428

Zugangs-URL: https://doaj.org/article/4e54cec0310e40fd8f68d954c73d3630

A multi-intent-aware recommendation algorithm based on interactive graph convolutional networks.

Autoren: Zhang, Junsan Gao, Hui Xiao, Sen et al.

Quelle: Complex & Intelligent Systems; Jun2024, Vol. 10 Issue 3, p4493-4506, 14p

Schlagwörter: GRAPH neural networks, RECOMMENDER systems, ALGORITHMS, FEATURE extraction, GRAPH algorithms, MATHEMATICAL convolutions

Recommendation Algorithm Integrating CNN and Attention System in Data Extraction.

Autoren: Yang Li Fei Yin Xianghui Hui

Quelle: Computers, Materials & Continua; 2023, Vol. 75 Issue 2, p4047-4063, 17p

Schlagwörter: DATA extraction, CONVOLUTIONAL neural networks, ALGORITHMS, FEATURE extraction

Light Graph Convolutional Recommendation Algorithm Based on Hybrid Spreading.

Autoren: Duan, Yaowei Zhang, Liang Lu, Xu et al.

Quelle: Applied Sciences (2076-3417); Feb2025, Vol. 15 Issue 4, p1898, 16p

Schlagwörter: RECOMMENDER systems, ALGORITHMS, INTERNET, MATRICES (Mathematics)

Aspect-based Sentiment Analysis with Ontology-assisted Recommender System on Multilingual Data using Optimised Self-attention and Adaptive Deep Learning Network.

Autoren: Nagelli, Archana Saleena, B.

Quelle: Journal of Information & Knowledge Management. Jun2025, Vol. 24 Issue 3, p1-44. 44p.

Schlagwörter: *Sentiment analysis, *Recommender systems, *Transformer models, *Search algorithms, *Natural language processing, Heuristic algorithms

Lbgcn: Lightweight bilinear graph convolutional network with attention mechanism for recommendation: LBGCN: Lightweight Bilinear Graph Convolutional...: Y. Su et al.

Autoren: Su, Yu Wei, Pingzhu Zhu, Linbo et al.

Quelle: Applied Intelligence; Apr2025, Vol. 55 Issue 6, p1-17, 17p

Enhancing Learning Recommendations in MOOC Search Engines through Named Entity Recognition

Autoren: Abdelmadjid Benmachiche Abdelhadi Sahia Soundes Oumaima Boufaida et al.

Quelle: Education and Information Technologies. 2025 30(9):13041-13071.

Peer Reviewed: Y

Page Count: 31

Descriptors: MOOCs, Natural Language Processing, Artificial Intelligence, Search Engines, Algorithms, Networks, Learning Processes, Short Term Memory, Data Interpretation, Educational Technology

Contribution to the enhancement of IoT-based application development and optimization of underwater communications, by artificial intelligence, edge computing, and 5G networks and beyond, in smart cities/seas

Autoren: Cepeda Pacheco, Juan Carlos

Weitere Verfasser: University/Department: Universitat Politècnica de Catalunya. Departament d'Enginyeria Telemàtica

Thesis Advisors: Domingo Aladrén, Mari Carmen

Quelle: TDX (Tesis Doctorals en Xarxa)

Schlagwörter: Àrees temàtiques de la UPC::Enginyeria de la telecomunicació, Àrees temàtiques de la UPC::Informàtica

Time: 621.3

Dateibeschreibung: application/pdf

Zugangs-URL: http://hdl.handle.net/10803/693321
https://dx.doi.org/10.5821/dissertation-2117-422056