ResNetMF : Improving Recommendation Accuracy and Speed with Matrix Factorization Enhanced by Residual Networks
Background: Recommendation systems are essential for personalized user experiences but struggle to balance accuracy and efficiency. Objective: This paper presents ResNetMF, an innovative hybrid framework designed to address these limitations by combining the strengths of matrix factorization (MF)...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | en |
| Published: |
University of Economics, Prague.
2025
|
| Subjects: | |
| Online Access: | http://ir.unimas.my/id/eprint/49388/1/Aip_001069_fin-0002.pdf http://ir.unimas.my/id/eprint/49388/ https://aip.vse.cz/getrevsrc.php?identification=public&mag=aip&raid=1069&type=fin&ver=2 https://doi.org/10.18267/j.aip.280 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|