Online product recommendation system using gated recurrent unit with Broyden Fletcher Goldfarb Shanno algorithm-Reference-Cited by-同舟云学术

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Online product recommendation system using gated recurrent unit with Broyden Fletcher Goldfarb Shanno algorithm

Published:2021-03-25 Issue: Volume: Page:
ISSN:1864-5909
Container-title:Evolutionary Intelligence
language:en
Short-container-title:Evol. Intel.

Author:

Suresh A.,Carmel Mary Belinda M. J.

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Cognitive Neuroscience,Computer Vision and Pattern Recognition,Mathematics (miscellaneous)

Link

http://link.springer.com/content/pdf/10.1007/s12065-021-00594-x.pdf

Reference38 articles.

1. Sohail SS, Siddiqui J, Ali R (2015) User feedback based evaluation of a product recommendation system using rank aggregation method. In: Advances in intelligent informatics. Springer, Cham, pp 349–358. sshttps://doi.org/10.1007/978-3-319-11218-3_32

2. Tsao WY (2013) The fitness of product information: Evidence from online recommendations. Int J Inf Manag 33:1–9. https://doi.org/10.1016/j.ijinfomgt.2012.04.003

3. Yesodha K, Anitha R, Mala T, Vindhya S (2018) Product recommendation system using support vector machine. In: Advanced computational and communication paradigms. Springer, Singapore, pp 438–446

4. An J, Zhao S, Lu X, Liu N (2018) A two-stage multiple-factor aware method for travel product recommendation. Multimedia Tools Appl 77:28991–29012. https://doi.org/10.1007/s11042-018-5992-7

5. Zhao WX, Wang J, He Y, Wen JR, Chang EY, Li X (2016) Mining product adopter information from online reviews for improving product recommendation. ACM Trans Knowl Discov Data TKDD 10:1–23. https://doi.org/10.1145/2842629

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