Location
Hilton Waikoloa Village, Hawaii
Event Website
https://hicss.hawaii.edu/
Start Date
7-1-2025 12:00 AM
End Date
10-1-2025 12:00 AM
Description
Context-aware Machine Learning (ML) is a well-established subfield of ML research. Here, the idea is to consider the context of an ML problem to enhance model performance. Here, the decisive factor is precise implementation of representative context data. The field of ML-driven support ticket classification has yet only scratched the potential of context awareness. Traditional text-only classification often lack accuracy, posing a challenge to the deployment of ML-automated support ticket classifiers. To address this, we explore four approaches in this paper integrating structured contextual data with textual descriptions. We found that correct incorporation of context data significantly enhances accuracy. We evaluate the effect of text embedding on model performance, highlighting the need for thoughtful data integration strategies. We demonstrate the effectiveness of context-aware approaches and explores alternative text preprocessing techniques. Closing, we discuss our findings – especially, the impact of data handling, data imbalance and interpretability on the overall automation project.
Recommended Citation
Fuchs, Simon; Wiehl, Nathalie; Wittges, Holger; and Krcmar, Helmut, "Customizing Context: Discovering the Optimal Integration of Context Data to Elevate ML-Driven Automated Support Ticket Classification" (2025). Hawaii International Conference on System Sciences 2025 (HICSS-58). 2.
https://aisel.aisnet.org/hicss-58/da/service_analytics/2
Customizing Context: Discovering the Optimal Integration of Context Data to Elevate ML-Driven Automated Support Ticket Classification
Hilton Waikoloa Village, Hawaii
Context-aware Machine Learning (ML) is a well-established subfield of ML research. Here, the idea is to consider the context of an ML problem to enhance model performance. Here, the decisive factor is precise implementation of representative context data. The field of ML-driven support ticket classification has yet only scratched the potential of context awareness. Traditional text-only classification often lack accuracy, posing a challenge to the deployment of ML-automated support ticket classifiers. To address this, we explore four approaches in this paper integrating structured contextual data with textual descriptions. We found that correct incorporation of context data significantly enhances accuracy. We evaluate the effect of text embedding on model performance, highlighting the need for thoughtful data integration strategies. We demonstrate the effectiveness of context-aware approaches and explores alternative text preprocessing techniques. Closing, we discuss our findings – especially, the impact of data handling, data imbalance and interpretability on the overall automation project.
https://aisel.aisnet.org/hicss-58/da/service_analytics/2