Digital game distribution platforms have captured the majority of the market in the game industry since the introduction of intelligent devices and the Internet. Customers adhering to the forum is a significant issue for game platforms. The sticking point is a personalized game recommendation that is tailored to the user’s preferences. By analyzing customer preferences and recommending appropriate game lists, the game platform could gain a competitive advantage over competitors. However, because of the numerous factors that must be considered, creating a personalized game list is difficult. This study considers many complementary elements required for a game recommendation, such as games and players, external and internal, crowd and individual. We propose a game recommendation mechanism that incorporates personality traits, social relationships, and crowds’ opinions. The recommendation method takes into account the semantics of the community, social tagging, and user behavior. This study aims to assist game platforms in developing game recommendations based on a broader range of factors, allowing users to access more great games tailored to their preferences.