The ultimate recommendation system: proposed Pranik System-Reference-Cited by-同舟云学术

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The ultimate recommendation system: proposed Pranik System

Published:2023-10-16 Issue: Volume: Page:
ISSN:1380-7501
Container-title:Multimedia Tools and Applications
language:en
Short-container-title:Multimed Tools Appl

Author:

Kumar Vipin,Gupta Amit Kumar,Garg Ruchi Rani,Kumar Nikhil,Kumar Rajeev^ORCID

Publisher

Springer Science and Business Media LLC

Subject

Computer Networks and Communications,Hardware and Architecture,Media Technology,Software

Link

https://link.springer.com/content/pdf/10.1007/s11042-023-17370-x.pdf

Reference29 articles.

1. Jiang L, Cheng Y, Yang Li, Li J, Yan H, Wang X (2018) A trust-based collaborative filtering algorithm for e-commerce recommendation system. J Ambient Intell Humaniz Comput 10(8):3023–3034. https://doi.org/10.1007/s12652-018-0928-7

2. Iwahama K, Hijikata Y, Nishida S (2004) “Content-based filtering system for Music Data,” 2004 International Symposium on Applications and the Internet Workshops. 2004 Workshops. doi:https://doi.org/10.1109/saintw.2004.1268677

3. Wang H, Zhang P, Lu T, Gu H, Gu N (2017) “Hybrid recommendation model based on incremental collaborative filtering and content-based algorithms,” 2017 IEEE 21st International Conference on Computer Supported Cooperative Work in Design (CSCWD). doi:https://doi.org/10.1109/cscwd.2017.8066717

4. He X, Liao L, Zhang H, Nie L, Hu X, Chua (2017) “Neural collaborative filtering,” Proceedings of the 26th International Conference on World Wide Web. doi:https://doi.org/10.1145/3038912.3052569

5. Sharma P, Yadav L (2020) Movie recommendation system using item based collaborative filtering. Int J Innov Res Comput Sci Technol 8(4). doi:https://doi.org/10.21276/ijircst.2020.8.4.2

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