Paper Details

Name: Pragya Rajput, Prikshit Singh, Arnav Kumar & Yoginder Singh
Country: India

Year: 2026
Volume: Volume No: 13, January, Year: 2026 (Special Issue)
Page Number: 217-225
DOI: https://doi.org/10.5281/zenodo.18976198

ABSTRACT
This research addresses the limitations of traditional research algorithms, such as data scarcity, cold-start Problems, scalability challenges, and inefficient ranking. The proposed optimization framework integrates hybrid recommender systems, deep learning-based ranking methods, and a cluster-based algorithm selection mechanism to enhance user experience and retrieval efficiency. It employs collaborative filtering, content-based filtering, and hybrid approaches while leveraging ranking metrics like Rank-Biased Precision (RBP) and Normalized Discounted Cumulative Gain (NDCG) to improve content relevance. Additionally, a network-friendly optimization model enhances computational efficiency without compromising search quality. Experimental evaluation on real-world datasets, including financial product recommendations and digital libraries, demonstrates significant improvements in precision, recall, and user satisfaction. The research introduces a personalized, adaptive approach that optimizes recommendation algorithms, bridging computational efficiency with enhanced user engagement for next-generation intelligent systems.

Keywords—Research Algorithm Optimization, Machine Learning, Recommender Systems, User Experience Enhancement, Ranking-based Optimization, Deep Learning, Adaptive Research Models