The idea that people tend to like games that are alike is intuitive, even obvious. But is it true? Like many intuitive ideas, it may be wrong, and it could be challenging to test. While it is relatively straightforward to test how well a particular notion of game likeness predicts which games an individual will like, the difficulty lies in developing such a conceptualization that is robust enough to handle all types of likeness. In this paper, we propose game relatedness, which we argue is more robust than the dominant top-down notion, commercial game genre. Borrowing from the concept in computational linguistics of semantic relatedness, games are related to the degree that one calls to mind the other. Having this notion, we operationalize it by a latent semantic analysis model, which we then use to build a game recommender system that recommends the games that are most related to the ones that a person already likes. Using a conventional recommender evaluation scheme, we find that our system recommends games at an accuracy well above chance, indicating that people tend to like related games.