A fine-grained social network recommender system
Author:
Publisher
Springer Science and Business Media LLC
Subject
Computer Science Applications,Human-Computer Interaction,Media Technology,Communication,Information Systems
Link
http://link.springer.com/content/pdf/10.1007/s13278-019-0621-7.pdf
Reference55 articles.
1. Aivazoglou M, Roussos O, Ioannidis S, Spiliotopoulos D, Polakis J (2017) Reveal: fine grained recommendations in online social networks. In: Proceedings of the 2017 IEEE/ACM international conference on advances in social networks analysis and mining (ASONAM 2017), Sydney, pp 1–4
2. Antonakaki D, Spiliotopoulos D, Samaras CV, Pratikakis P, Ioannidis S, Fragopoulou P (2017) Social media analysis during political turbulence. PLoS ONE 12(10):1–23
3. Bakshy E, Eckles D, Yan R, Rosenn I (2012) Social influence in social advertising: evidence from field experiments. In: Proceedings of the 13th ACM conference on electronic commerce, Valencia, pp 146–161
4. Berkovsky S, Freyne J (2015) Web personalization and recommender systems. In: Proceedings of the 21st ACM SIGKDD international conference on knowledge discovery and data mining, Sydney, pp 2307–2308
5. Bird S, Loper E, Klein E (2009) Natural language processing with Python. O’Reilly Media Inc, Sebastopol
Cited by 41 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. Beyond accuracy measures: the effect of diversity, novelty and serendipity in recommender systems on user engagement;Electronic Commerce Research;2024-02-18
2. Contextual Semantics Interaction Graph Embedding Learning for Recommender Systems;IEEE Transactions on Computational Social Systems;2024
3. Intelligent recommendation algorithm for social networks based on improving a generalized regression neural network;Electronic Research Archive;2024
4. enemos-p: An enhanced emotion specific prediction for recommender systems;Expert Systems with Applications;2023-10
5. Using AI–ML to Augment the Capabilities of Social Media for Telehealth and Remote Patient Monitoring;Healthcare;2023-06-10
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