Enhancing Information Architecture with Machine Learning for Digital Media Platforms

Name

Taylor N. Mietzner, Georgia Southern UniversityFollow

Publication Date

4-1-2024

Major

Information Technology (B.S.)

Document Type and Release Option

Thesis (open access)

Faculty Mentor

Dr. Christopher Kadlec

Abstract

Modern advancements in machine learning are transforming the technological landscape, including information architecture within user experience design. With the unparalleled amount of user data generated on online media platforms and applications, an adjustment in the design process to incorporate machine learning for categorizing the influx of semantic data while maintaining a user-centric structure is essential. Machine learning tools, such as the classification and recommendation system, need to be incorporated into the design for user experience and marketing success. There is a current gap between incorporating the backend modeling algorithms and the frontend information architecture system design together. The aim of this research is to discover how machine learning technology can enhance the information architecture for user experience across multimedia platforms. For practical demonstration, there will be a novel, proposed information architecture model with machine learning input to recommend a topic selection system presented on the user-facing design.

Recommended Citation

Mietzner, Taylor N., "Enhancing Information Architecture with Machine Learning for Digital Media Platforms" (2024). Honors College Theses. 929.
https://digitalcommons.georgiasouthern.edu/honors-theses/929