P-MOIA-RS: a multi-objective optimization and decision-making algorithm for recommendation systems-Reference-Cited by-同舟云学术

P-MOIA-RS: a multi-objective optimization and decision-making algorithm for recommendation systems

Published:2020-05-22 Issue:1 Volume:12 Page:443-454
ISSN:1868-5137
Container-title:Journal of Ambient Intelligence and Humanized Computing
language:en
Short-container-title:J Ambient Intell Human Comput

Author:

Chai Zhengyi^ORCID,Li Yalun,Zhu Sifeng

Funder

National Outstanding Youth Science Fund Project of National Natural Science Foundation of China

Publisher

Springer Science and Business Media LLC

Subject

General Computer Science

Link

https://link.springer.com/content/pdf/10.1007/s12652-020-01997-x.pdf

Reference38 articles.

1. Acilar AM, Arslan A (2009) A collaborative filtering method based on artificial immune network. Expert Syst Appl 36(4):8324–8332

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

3. Aggarwal CC (2016) An introduction to recommender systems. Recommender systems. Springer, Berlin

4. Bradley K, Smyth B (2001) Improving recommendation diversity. In: Proceedings of the Twelfth Irish conference on Artificial intelligence and cognitive science, Maynooth, Ireland, pp. 85–94

5. Cacheda F, Formoso V (2011) Comparison of collaborative filtering algorithms: limitations of current techniques and proposals for scalable, high-performance recommender systems. ACM Trans Web 5(1):1–33

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