BYU

Abstract by Jacob Carter

Personal Infomation


Presenter's Name

Jacob Carter

Co-Presenters

None

Degree Level

Undergraduate

Co-Authors

None

Abstract Infomation


Department

Computer Science

Faculty Advisor

Dennis Ng

Title

Automated Video Game Categorization

Abstract

With such a large quantity of video games on the market it can be challenging for individuals to decide where to invest their time and money to have an enjoyable video gaming experience. In order to minimize the time, efforts, and expenses of gamers invested in games they will enjoy, we propose to develop a recommendation algorithm that can automatically classify video games into the various gaming genres and recommend games to the gamers based on their gaming genre preferences. We experimented using a Support Vector Machine (SVM) approach, the LIBSVM implementation to be exact, in order to automate the game classification process. SVMs generally categorize effectively thanks to their ability to map the data to higher planes. Our results showed an average 85% prediction accuracy. While more research is needed, the SVM appears to be a good choice for automated video game categorization.