International Journal of Information Engineering and Electronic Business (IJIEEB)
IJIEEB Vol. 18, No. 4, 8 Aug. 2026
Cover page and Table of Contents: PDF (size: 1697KB)
Enhanced Educational Recommendations via Feedback-Refined Learning-to-Rank with XGBoost
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Author(s)
Sushilkumar Chavhan
1,*
Ujwalla Gawande
1
Sachin Jain
2
Nikhil Manglurkar
3
Rajesh Dharmik
1
Devendra Shahare
4
Index Terms
Personalized Recommendation System, Learning-to-Rank, XGBoost, Course Ranking, User Feedback, Educational Platforms
Abstract
A system that is personalized and capable of automatically suggesting appropriate courses based on a user's particular questions and interests provides customized recommendations. The system employs an LTR model relying on XGBoost to learn relationships between the queries and the courses. Dynamic ranking feature refinement enhances ranking, and a feedback loop constructs incrementally improving the recommendations by applying relevant courses to update the model. The scraped educational sites are the foundation of the dataset, where there are granular course descriptions as well as the interaction logs. Evaluation results indicate that the system is able to produce high ranking outcomes as evidenced by an NDCG value of 0.85 and high values for MRR. The system is able to produce low query processing latency, making it possible for real-time responsiveness. User feedback analysis following system retraining indicated a 90% increase in user satisfaction. The suggested framework is dynamic and provides personalized recommendations for courses in various learning environments.
Cite This Paper
Sushilkumar Chavhan, Ujwalla Gawande, Sachin Jain, Nikhil Manglurkar, Rajesh Dharmik, Devendra Shahare, "Enhanced Educational Recommendations via Feedback-Refined Learning-to-Rank with XGBoost", International Journal of Information Engineering and Electronic Business(IJIEEB), Vol.18, No.4, pp. 176-193, 2026. DOI:10.5815/ijieeb.2026.04.12
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