SMU Data Science Review
Abstract
Smaller game studios are at a disadvantage when it comes to getting their product noticed by users. This study aims to provide insights on how recommendation engines work so that these smaller studios can have their games noticed on Steam. Steam is one of the largest video game distribution services and they have a recommendation engine which promotes games to its user base. This study utilized user information such as number of games played, the type of games, and the hours played and created recommendation engines to identify the qualities in the game that are driving recommendations.
Recommended Citation
Blue, Robert; Garcia, Luis; and Turner, Jacob
(2024)
"Game Recommendation Analysis Using Steam Profiles and Reviews,"
SMU Data Science Review: Vol. 8:
No.
1, Article 4.
Available at:
https://scholar.smu.edu/datasciencereview/vol8/iss1/4
Creative Commons License
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