A hybrid knowledge-based machine learning approach for ticket classification in the AWS cloud

Automated classification of customer support tickets is vital for modern service desks to reduce workload and improve response times. Machine learning (ML) methods provide flexibility and accuracy but often lack explainability and incur high computational costs. Knowledge-based systems (KBS) offer transparency and determinism but struggle with ambiguous or unseen inputs. This paper presents a hybrid approach combining a lightweight rule-based classifier with a fallback ML model, deployed in a scalable serverless AWS environment. We evaluated logistic regression with TF-IDF, sentence embedding classifiers, and the hybrid model on a curated multilingual support dataset using accuracy, F1 score, precision, and recall. Logistic regression outperformed embedding-based classifiers, achieving the highest macro-averaged F1 scores. The hybrid system leverages domain-specific rules to handle high-confidence cases and defers to ML when rules do not apply, consistently outperforming standalone ML models. Additional experiments tested robustness across multiple runs, train-test splits, and cross-lingual generalization with German data. Deployed using AWS Lambda, S3, and DynamoDB, the solution balances low latency and scalability. The results demonstrate the hybrid approach’s practical advantages for reliable, efficient support ticket classification.