Enhancing sparse data recommendations with self-inspected adaptive SMOTE and hybrid neural networks

Abstract Personalized recommendation systems are vital for enhancing user satisfaction and reducing information overload, especially in data-sparse environments like e-commerce platforms. This paper introduces a novel hybrid framework that combines Long Short-Term Memory (LSTM) with a modified Split...

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Bibliographic Details
Main Authors:	Ramesh Vatambeti, Hari Prasad Gandikota, D. Siri, G. Satyanarayana, Narasimhula Balayesu, M. Ganesh Karthik, Koteswararao Ch
Format:	Article
Language:	English
Published:	Nature Portfolio 2025-05-01
Series:	Scientific Reports
Subjects:	Personalized recommendation systems Long short-term memory Quokka swarm optimization Hybrid mutation based white shark optimizer Electronic publishing companies User information
Online Access:	https://doi.org/10.1038/s41598-025-02593-9
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Enhancing sparse data recommendations with self-inspected adaptive SMOTE and hybrid neural networks

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