Improve the recommendation using hybrid tendency and user trust-Reference-Cited by-同舟云学术

Improve the recommendation using hybrid tendency and user trust

Published:2023-07-29 Issue:6 Volume:15 Page:3147-3156
ISSN:2511-2104
Container-title:International Journal of Information Technology
language:en
Short-container-title:Int. j. inf. tecnol.

Author:

Kumar Jitendra,Yannam V. Ramanjaneyulu,Prajapati Harshit,Sahoo Bibhudatta,Babu Korra Sathya

Publisher

Springer Science and Business Media LLC

Subject

Electrical and Electronic Engineering,Applied Mathematics,Artificial Intelligence,Computational Theory and Mathematics,Computer Networks and Communications,Computer Science Applications,Information Systems

Link

https://link.springer.com/content/pdf/10.1007/s41870-023-01377-6.pdf

Reference23 articles.

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3. Cacheda F, Carneiro V, Fernández D, Formoso V (2011) Comparison of collaborative filtering algorithms: limitations of current techniques and proposals for scalable, high-performance recommender systems. ACM Transactions on the Web (TWEB), ACM, pp 1–33

4. Sreepada RS, Patra BK, Chakrabarty A, Chandak S (2018) Revisiting tendency based collaborative filtering for personalized recommendations. In: Proceedings of the ACM India joint international conference on data science and management of data. ACM, pp 230–239

5. Bellogín A, Castells P, Cantador I (2014) Neighbor selection and weighting in user-based collaborative filtering: a performance prediction approach. ACM Transactions on the Web (TWEB), ACM, pp 1–30

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