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http://dx.doi.org/10.9723/jksiis.2018.23.6.009

Game Recommendation System Based on User Ratings  

Kim, JongHyen (금오공과대학교 컴퓨터소프트웨어공학과)
Jo, HyeonJeong (금오공과대학교 컴퓨터소프트웨어공학과)
Kim, Byeong Man (금오공과대학교 컴퓨터소프트웨어공학과)
Publication Information
Journal of Korea Society of Industrial Information Systems / v.23, no.6, 2018 , pp. 9-19 More about this Journal
Abstract
As the recent developments in the game industry and people's interest in game streaming become more popular, non-professional gamers are also interested in games and buying them. However, it is difficult to judge which game is the most enjoyable among the games released in dozens every day. Although the game sales platform is equipped with the game recommendation function, it is not accurate because it is used as a means of increasing their sales and recommending users with a focus on their discount products or new products. For this reason, in this paper, we propose a game recommendation system based on the users ratings, which raises the recommendation satisfaction level of users and appropriately reflect their experience. In the system, we implement the rate prediction function using collaborative filtering and the game recommendation function using Naive Bayesian classifier to provide users with quick and accurate recommendations. As the result, the rate prediction algorithm achieved a throughput of 2.4 seconds and an average of 72.1 percent accuracy. For the game recommendation algorithm, we obtained 75.187 percent accuracy and were able to provide users with fast and accurate recommendations.
Keywords
Game Recommendation; Collaborative Filtering; Naive Bayesian Classifier;
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Times Cited By KSCI : 1  (Citation Analysis)
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