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Journal of Information and Optimization Sciences cover
Open Access ·Peer-reviewed·ISSN (Online): 2169-0103·ISSN (Print): 0252-2667

WoS  JIF 2026 : 0.4 (Q4)

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Monthly Journal: Publishes theoretical and applied research on topics in information and optimization sciences.

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Open Access Research Article

A machine learning approach for personalized course recommendation systems for learners

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pp. 1–11Online FirstJune 2026DOI: 10.47974/JIOS-2154XML
Received:
01 Mar 2025
Published Online:
30 Jun 2026
Article type:
Research Article
Language:
EN
Article no.:
JIOS-2154
Pages:
1–11

Abstract

This paper presents a novel approach to enhancing collaborative filtering for course recommendations by integrating Hybrid Fruit-fly Optimized Density-Based K-Means Clustering (HFO-KMC) with Atten-BERT-RU and a Coordinate Attention Module (CAM). The methodology improves user grouping and feature representation, resulting in more accurate and personalized recommendations. HFO-KMC optimizes clustering by reducing errors, while Atten-BERT-RU with CAM enhances feature extraction from course reviews. The system was validated using Coursera course reviews, showing notable improvements in recommendation accuracy and diversity over traditional models. The results highlight the effectiveness of combining optimized clustering with deep learning techniques, making this approach a significant advancement in recommendation systems.

Keywords

Subject Classifications

68T0568T20

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