Degree Type

Creative Component

Semester of Graduation

Spring 2020

Department

Computer Science

First Major Professor

Carl K Chang

Degree(s)

Master of Science (MS)

Major(s)

Human Computer Interaction

Abstract

Previous studies in this domain have discussed about building single-platform mood-based recommender systems for general users using computer technologies using typical algorithmic or emerging machine learning approach. However, they were no researches proposed to build a cross-platform mood-based video recommender system for older adults using target users’ input as domain knowledge. This study proposed the design and implementation of a cross-platform mood-based video recommender system for older adults. The domain knowledge of which video content type to recommend was collected via conducting a video content type preferences survey based on various mood states with both direct and indirect groups. To enable this video recommender system to run on both Android and iOS operating systems with a single code base, the React Native framework was adopted to implement this system. In addition, this study focused on implementing a mobile application with a good user experience targeting older adults. A usability testing experiment with a user group was conducted after the recommender system was developed. In the end this report presents analysis results based on the video content type preferences survey, usability testing, and user satisfaction about this mood-based video recommender system.

Copyright Owner

Chen, Meifang

File Format

Word

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