YUEN Man-Ching, ConnieConnieYUEN Man-ChingYung, Chi-WaiChi-WaiYungCheng, Wing-FatWing-FatChengTsang, Hon PongHon PongTsangKwan, Chi HoChi HoKwanLi, Po YiPo YiLiChan, Chun LokChun LokChan2024-03-142024-03-142023Yuen, Man Ching, Yung, Chi Wai, Cheng, Wing Fat, Tsang, Hon Pong, Kwan, Chi Ho, Chan, Chun Lok & Li, Po Yi (2023). Game recommendation system. In Tallón-Ballesteros, A.J.& Beltrán-Barba, R. (Eds.). Fuzzy systems and data mining IX : Proceedings of FSDM 2023. FSDM 2023, Chongqing, China (pp. 843-857). IOS Press.97816436847039781643684710http://hdl.handle.net/20.500.11861/9020Open accessRecommendation systems are widely adopted in many areas to provide better services to customers. As there are many games stored in online games platforms, people may be confused when choosing or buying the game that is suitable for them. Many game platforms would like to have a game recommendation system, so that it can automatically recommend the right games to their customers. However, there are a lot of difficulties in developing game recommendation systems. First, it is difficult to collect and organize data on customers’ behavior. Second, the user interface needs to be easier to use for customers, such as the charts displayed that customers are interested in. In this paper, we have developed a games recommender system with complete functional recommending features. By using machine learning techniques and applying data visualization on our system, we build a recommendation system that can showcase flexible outcomes with the same element as the user input, which can give the user more choice when finding the games they want.enRecommendation SystemGameMachine LearningWeb Scraping and Data VisualizationConference Paper10.3233/FAIA231096