Game Recommendation Using Content-based Algorithm Title
Zakaria Berlam Pragusma, Vanessa Kwandinata, Jevent Natthannael, Meiliana Meiliana, Said Achmad · 2023
The gaming industry is currently experiencing rapid growth, with an estimated 2.96 billion games available worldwide. This substantial expansion poses a challenge for gamers in choosing which game to play due to the overwhelming number of options. To address this issue, this paper proposes a content-based game recommendation system. The system utilizes user input, including games the user has played before or expressed interest in, along with personal ratings. This input is used to determine which games are more likely to be enjoyed by the user. The system creates a user profile based on the similarity between previously selected items, using categories, genre, and developer as key attributes. These attributes have been identified as the most influential factors for gamers in deciding whether to play a game or not. The accuracy that is given by this model reached 82% fit the user’s interest.