Leveraging Content-Based Filtering for Personalized Game Recommendations: A Flutter-Based Mobile Application Development
Abstract
The background of this study stems from the need for a recommendation system to assist users in finding games that match their interests. With the rapid growth of the gaming market, an increasing number of people engage in gaming activities. In 2022, the personal computer (PC) gaming market accounted for 37.9% of all gamers worldwide. One of the largest PC gaming platforms is Steam, developed by Valve Corporation, which boasts over 184 million active users. However, the overwhelming number of options can lead users to lose interest in purchasing games. Therefore, a recommendation system is required to help users find games that align with their preferences. The methods/theories employed in this study include data from the Steam Web API, SteamSpy API, and local JSON files. The Content-Based Filtering method, using the Cosine Similarity algorithm, was implemented to determine the similarity index between games and user preferences. Flutter was used for application development and to display the recommendation results to users. The results of this study show that the application was successfully developed, and the Content-Based Filtering method provided recommendations that met expectations. The highest cosine similarity factor achieved was 0.6454972244, indicating a fairly good level of accuracy. Application evaluation using the Technology Acceptance Model revealed positive reception, with a "Perceived Usefulness" score of 82.6% and a "Perceived Ease of Use" score of 86.2%, indicating that users found the application both useful and easy to use.
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