Deep learning feature selection to unhide demographic recommender systems factors
Author:
Funder
Ministerio de Ciencia e Innovación
Publisher
Springer Science and Business Media LLC
Subject
Artificial Intelligence,Software
Link
https://link.springer.com/content/pdf/10.1007/s00521-020-05494-2.pdf
Reference61 articles.
1. Al-Shamri MYH (2016) User profiling approaches for demographic recommender systems. Knowl Based Syst 100:175–187
2. Banan A, Nasiri A, Taheri-Garavand A (2020) Deep learning-based appearance features extraction for automated carp species identification. Aquac Eng 89:102053
3. Barragáns-Martínez AB, Costa-Montenegro E, Burguillo JC, Rey-López M, Mikic-Fonte FA, Peleteiro A (2010) A hybrid content-based and item-based collaborative filtering approach to recommend TV programs enhanced with singular value decomposition. Inf Sci 180(22):4290–4311. https://doi.org/10.1016/j.ins.2010.07.024
4. Batmaz Z, Yurekli A, Bilge A, Kaleli C (2019) A review on deep learning for recommender systems: challenges and remedies. Artif Intell Rev 52(1):1–37
5. Bharadhwaj H, Joshi S (2018) Explanations for temporal recommendations. Künstl Intell 32(4):267–272. https://doi.org/10.1007/s13218-018-0560-x
Cited by 19 articles. 订阅此论文施引文献 订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献
1. IUG-CF: Neural collaborative filtering with ideal user group labels;Expert Systems with Applications;2024-03
2. Wasserstein GAN-based architecture to generate collaborative filtering synthetic datasets;Applied Intelligence;2024-02
3. Personalized resource recommendation method of student online learning platform based on LSTM and collaborative filtering;Journal of Intelligent Systems;2024-01-01
4. Enhancing Movie Recommendations: An Ensemble-Based Deep Collaborative Filtering Approach Utilizing AdaMVRGO Optimization;Traitement du Signal;2023-12-30
5. Deep learning-based collaborative filtering recommender systems: a comprehensive and systematic review;Neural Computing and Applications;2023-10-05
1.学者识别学者识别
2.学术分析学术分析
3.人才评估人才评估
"同舟云学术"是以全球学者为主线，采集、加工和组织学术论文而形成的新型学术文献查询和分析系统，可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容，当前同舟云学术共收录了国内外主流学术期刊6万余种，收集的期刊论文及会议论文总量共计约1.5亿篇，并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询！咨询电话：010-8811{复制后删除}0370
www.globalauthorid.com
TOP
Copyright © 2019-2024 北京同舟云网络信息技术有限公司 京公网安备11010802033243号 京ICP备18003416号-3