Degree Name
MS (Master of Science)
Program
Computer Science
Date of Award
5-2026
Committee Chair or Co-Chairs
Brian Bennett
Committee Members
Shaik Shehenaz, Khan Mohammad Shoeb
Abstract
Supplier selection is an important supply chain activity traditionally reliant on subjective, simple scoring methods and human judgment. This thesis frames selection as a recommendation problem, similar to how online platforms suggest products. Context features were utilized to identify optimal supplier matches. An offline evaluation compared K-Nearest Neighbors (K-NN), Random Forest, and Dual Encoder models across temporal and random data splits. Results indicate that K-NN achieved the highest ranking accuracy, proving that contract similarity is a powerful proxy for relevance in sparse datasets. Conversely, the Dual Encoder showed lower accuracy but wider coverage, highlighting a greater ability to generalize well. The Random Forest performed poorly due to sparse feature sets. The findings show that model performance is limited by unseen suppliers and reveal a key trade-off between ranking precision and supplier exploration. Ultimately, aligning model architecture with specific procurement objectives is essential as an effective decision support system.
Document Type
Thesis - embargo
Recommended Citation
Hardi, Aswad M., "Supplier Selection Recommendation System: A Comparative Study of K-Nearest Neighbors, Random Forest, and Dual-Encoder Models" (2026). Electronic Theses and Dissertations. Paper 4700. https://dc.etsu.edu/etd/4700
Copyright
Copyright by the authors.
