Design of electronic-commerce recommendation systems based on outlier mining-Reference-Cited by-同舟云学术

登录注册会员服务联系我们

Design of electronic-commerce recommendation systems based on outlier mining

Published:2020-08-06 Issue:2 Volume:31 Page:295-311
ISSN:1019-6781
Container-title:Electronic Markets
language:en
Short-container-title:Electron Markets

Author:

Xia Huosong,Wei Xiang,An Wuyue,Zhang Zuopeng Justin^ORCID,Sun Zelin

Funder

National Natural Science Foundation of China

Research center of Enterprise Decision Support, Key Research Institute of Humanities and Social Sciences in Universities of Hubei Province

Publisher

Springer Science and Business Media LLC

Subject

Management of Technology and Innovation,Marketing,Computer Science Applications,Economics and Econometrics,Business and International Management

Link

https://link.springer.com/content/pdf/10.1007/s12525-020-00435-2.pdf

Reference56 articles.

1. Adomavicius, G., & Tuzhilin, A. (2005). Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions. IEEE Transactions on Knowledge & Data Engineering, 17(6), 734–749. https://doi.org/10.1109/TKDE.2005.99 .

2. Ahn, H. J. (2008). A new similarity measure for collaborative filtering to alleviate the new user cold-starting problem. Information Sciences, 178(1), 37–51. https://doi.org/10.1016/j.ins.2007.07.024 .

3. Ahn, J., Lee, M. H., & Lee, J. A. (2019). Distance-based outlier detection for high dimension, low sample size data. Journal of Applied Statistics, 46(1), 13–29. https://doi.org/10.1080/02664763.2018.1452901 .

4. Ai, D. X., Zuo, H., & Yang, J. (2013). C2C e-commerce recommender system based on three-dimensional collaborative filtering. Applied Mechanics and Materials, 336–338, 2563–2566. https://doi.org/10.4028/www.scientific.net/amm.336-338.2563

5. Akter, S., & Wamba, S. F. (2016). Big data analytics in E-commerce: A systematic review and agenda for future research. Electronic Markets, 26(2), 173–194. https://doi.org/10.1007/s12525-016-0219-0 .

Cited by 14 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. Recommendation System: A transformative Artificial Intelligence Tool for E-commerce;2024 7th International Conference on Informatics and Computational Sciences (ICICoS);2024-07-17

2. Knowledge evolutionary process of Artificial intelligence in E-commerce: Main path analysis and science mapping analysis;Expert Systems with Applications;2024-03

3. Using movie genres and Demographic Information to improve movie recommendation systems;Signal and Data Processing;2024-03-01

4. Developing a Recommendation System for Financial Management Using Collaborative Filtering and Sequencematcher;SSRN Electronic Journal;2024

5. Multichannel Consumer Service Integration of Recommendation Systems;Customer Centric Support Services in the Digital Age;2023-12-31

同舟云学术

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