Intelligent Course Recommendation: A Hybrid Deep Learning Perspective

Bys: Kolli, Chandra Sekhar, Vadisena, Venkata Krishna Reddy, Gottala, A. K. S. Rajeev Kumar

In an era where educational choices can overwhelm students, HHFHNet emerges as a groundbreaking solution for precise course recommendations. This comprehensive guide introduces readers to the innovative Hybrid HAN HDLTex Forward Harmonic Net (HHFHNet) architecture, a sophisticated system that combines the power of Hierarchical Attention Networks (HAN) and Hierarchical Deep Learning for Texts (HDLTex). Through detailed exploration of Term Frequency-Inverse Document Frequency (TF-IDF), ranking-based recommendations, and Explainable Artificial Intelligence (XAI), readers will master the intricacies of building intelligent course recommendation systems. The book presents a novel approach to educational guidance, incorporating content-based filtering, collaborative filtering, and hybrid methods to address the challenging cold-start problem. Whether you're an AI researcher, educational technologist, or academic institution developer, this essential resource provides the theoretical foundation and practical implementation strategies needed to revolutionize course selection processes.

• Format: Paperback
• Language: English
• ISBN: 6208440963
• ISBN13: 9786208440961
• Release Date: April 2025
• Publisher: LAP Lambert Academic Publishing
• Length: 64 Pages
• Weight: 0.22 lbs.
• Dimensions: 0.1" x 6.0" x 9.0"