云南高校图书馆联盟文献共享服务平台- RECOMMENDER

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Weighted Forwarding in Graph Convolution Networks for Recommendation Information Systems: 《Computers, Materials & Continua》2024年第2期1897-1914,共18页Sang-min Lee Namgi Kim; This work was supported by the Kyonggi University Research Grant 2022.; Recommendation Information Systems(RIS)are pivotal in helping users in swiftly locating desired content from the vast amount of information available on the Internet.Graph Convolution Network(GCN)algorithms have been ...; 关键词：Deep learning graph neural network graph convolution network graph convolution network model learning method recommender information systems

Enhancing Next-Item Recommendation Through Adaptive User Group Modeling: 《Journal of Social Computing》2023年第2期112-124,共13页Nengjun Zhu Lingdan Sun Jian Cao Xinjiang Lu Runtong Li; supported by the National Natural Science Foundation of China(No.62202282);Shanghai Youth Science and Technology Talents Sailing Program(No.22YF1413700).; Session-based recommender systems are increasingly applied to next-item recommendations.However,existing approaches encode the session information of each user independently and do not consider the interrelationship b...; 关键词：session-based recommender user group modeling attention mechanism adaptive learning

Combined Linear Multi-Model for Reliable Route Recommender in Next Generation Network: 《Intelligent Automation & Soft Computing》2023年第4期39-56,共18页S.Kalavathi R.Nedunchelian; Network analysis is a promisingﬁeld in the area of network applications as different types of trafﬁc grow enormously and exponentially.Reliable route prediction is a challenging task in the Large Scale Networks(LSN).V...; 关键词：Network embedding node classiﬁcation link prediction routing protocols novel RRPM

FedRec:Trusted rank-based recommender scheme for service provisioning in federated cloud environment被引量：1: 《Digital Communications and Networks》2023年第1期33-46,共14页Ashwin Verma Pronaya Bhattacharya Umesh Bodkhe Deepti Saraswat Sudeep Tanwar Kapal Dev; The emergence of on-demand service provisioning by Federated Cloud Providers(FCPs)to Cloud Users(CU)has fuelled significant innovations in cloud provisioning models.Owing to the massive traffic,massive CU resource req...; 关键词：Blockchain 5G-enhanced mobile broadband Federated clouds Rank-based recommender model Smart contracts

A multi-preference integrated algorithm(MPIA)for the deep learning-based recommender framework(DLRF): 《International Journal of Intelligent Computing and Cybernetics》2022年第4期625-641,共17页Vikram Maditham N.Sudhakar Reddy Madhavi Kasa; Purpose-The deep learning-based recommender framework(DLRF)is based on an improved long short-term memory(LSTM)structurewith additional controllers;thus,it considers contextual information for state transition.It also...; 关键词：Collaborative filtering Deep learning User preference integration Recommender systems

一流科技期刊审稿人系统建设的思考——基于Reviewer Locator和Reviewer Recommender审稿人推荐系统的分析: 《学报编辑论丛》2021年第1期542-546,共5页冯景; 随着中国的学术期刊不断走向国际化,对期刊送审质量的要求越来越高。好的审稿人推荐系统可以提高审稿效率和审稿质量,从而促进期刊学术水平和国际化水平的提高。文中介绍了两个英文审稿人推荐系统——ScholarOne系统的Reviewer Locator...; 关键词：审稿人推荐系统审稿系统国际化

Neural Explainable Recommender Model Based on Attributes and Reviews: 《Journal of Computer Science & Technology》2020年第6期1446-1460,共15页Yu-Yao Liu Bo Yang Hong-Bin Pei Jing Huang; This work was supported by the University Science and Technology Research Plan Project of Jilin Province of China under Grant No.JJKH20190156KJ;the National Natural Science Foundation of China under Grant Nos.61572226 and 61876069;Jilin Province Key Scientific and Technological Research and Development Project under Grant Nos.20180201067GX and 20180201044GX;Jilin Province Natural Science Foundation under Grant No.20200201036JC.; Explainable recommendation, which can provide reasonable explanations for recommendations, is increasingly important in many fields. Although traditional embedding-based models can learn many implicit features, result...; 关键词：recommender system explainable recommendation review usefulness attribute usefulness

Multi-feedback Pairwise Ranking via Adversarial Training for Recommender: 《Chinese Journal of Electronics》2020年第4期615-622,共8页WANG Jianfang FU Zhiyuan NIU Mingxin ZHANG Pengbo ZHANG Qiuling; Personalized recommendation systems predict potential demand by analyzing user preferences.Generally,user feedback information is inferred from implicit feedback or explicit feedback.Nevertheless,feedback can be conta...; 关键词：Adversarial training Pairwise ranking Collaborative filtering Recommender system

Exploiting Structural and Temporal Influence for Dynamic Social-Aware Recommendation: 《Journal of Computer Science & Technology》2020年第2期281-294,共14页Yang Liu Zhi Li Wei Huang Tong Xu En-Hong Chen; This work was partially supported by the National Key Research and Development Program of China under Grant No.2018YFB1402600;the National Natural Science Foundation of China under Grant Nos.61703386 and U1605251;the MSRA(Microsoft Research Asia)Collaborative Research Project.; Recent years have witnessed the rapid development of online social platforms,which effectively support the business intelligence and provide services for massive users.Along this line,large efforts have been made on t...; 关键词：RECOMMENDER system SOCIAL INFLUENCE sequential RECOMMENDATION

A survey of autoencoder-based recommender systems被引量：13: 《Frontiers of Computer Science》2020年第2期430-450,共21页Guijuan ZHANG Yang LIU Xiaoning JIN; This work was supported by Beijing Advanced Inno vation Center for Future Internet Technology(110000546617001).; In the past decade,recommender systems have been widely used to provide users with personalized products and services.However,most traditional recommender systems are still facing a challenge in dealing with the huge ...; 关键词：RECOMMENDER system autoencoder DEEP LEARNING DATA MINING

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