Unsupervised and supervised methods for the detection of hurriedly created profiles in recommender systems-Reference-Cited by-同舟云学术

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Unsupervised and supervised methods for the detection of hurriedly created profiles in recommender systems

Published:2020-04-30 Issue:9 Volume:11 Page:2165-2179
ISSN:1868-8071
Container-title:International Journal of Machine Learning and Cybernetics
language:en
Short-container-title:Int. J. Mach. Learn. & Cyber.

Author:

Panagiotakis Costas,Papadakis Harris,Fragopoulou Paraskevi

Funder

Operational Program Competitiveness, Entrepreneurship and Innovation, under the call RESEARCH - CREATE - INNOVATE

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Computer Vision and Pattern Recognition,Software

Link

https://link.springer.com/content/pdf/10.1007/s13042-020-01108-4.pdf

Reference44 articles.

1. Adomavicius G, Kwon Y (2012) Improving aggregate recommendation diversity using ranking-based techniques. IEEE Trans Knowl Data Eng 24(5):896–911

2. Adomavicius G, Tuzhilin A (2005) Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions. IEEE Trans Knowl Data Eng 17(6):734–749

3. Belgiu M, Drăguţ L (2016) Random forest in remote sensing: a review of applications and future directions. ISPRS J Photogramm Remote Sens 114:24–31

4. Bennett J, Lanning S, Netflix N (2007) The netflix prize. In: In KDD Cup and Workshop in conjunction with KDD

5. Breiman L (2001) Random forests. Mach Learn 45(1):5–32

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