Work

An Intelligent Hybrid AI Course Recommendation Framework Integrating BERT Embeddings and Random Forest Classification

Year: 2025

Type: article

Abstract: With the proliferation of online learning platforms, selecting appropriate artificial intelligence (AI) courses has become increasingly complex for learners. This study proposes a novel hybrid AI course recommendation framework that integrates Term Frequency-Inverse Document Frequency (TF-IDF) and Bidirectional Encoder Representations from Transformers (BERT) for robust textual feature extraction, enhanced by a Random Forest classifier to improve recommendation precision. A curated dataset of 2,238 AI-related courses from Udemy was constructed through multi-session web scraping, followed by comprehensive data preprocessing. The system computes semantic and lexical similarity using cosine similarity and fuzzy matching to handle user input variations. Experimental results demonstrate a high recommendation accuracy=91.25%, precision=96.63%, and F1-score=90.77%. Compared to baseline models, the proposed framework significantly improves performance in cold-start scenarios and does not rely on historical user interactions. A Flask-based web application was developed for real-time deployment, offering instant, user-friendly recommendations. This work contributes a scalable and metadata-driven AI recommender architecture with practical deployment and promising generalization capabilities.

Source: Computers

DOI: 10.3390/computers14090353

Authors: Armaneesa Naaman Hasoon, Salwa Khalid Abdulateef, Rafal Saleh Abdul Ameer, Moceheb Lazam Shuwandy

Institution: University of Tikrit

Language: English

Topic: Recommender Systems and Techniques

Subfield: Information Systems

Field: Computer Science

Domain: Physical Sciences

SDG: Quality education

Open Access status: gold

Estimated APC paid: $2,172