  Game recommendation is an important application of recommender systems.
Recommendations are made possible by data sets of historical player and game
interactions, and sometimes the data sets include features that describe games
or players. Collaborative filtering has been found to be the most accurate
predictor of past interactions. However, it can only be applied to predict new
interactions for those games and players where a significant number of past
interactions are present. In other words, predictions for completely new games
and players is not possible. In this paper, we use a survey data set of game
likes to present content based interaction models that generalize into new
games, new players, and both new games and players simultaneously. We find that
the models outperform collaborative filtering in these tasks, which makes them
useful for real world game recommendation. The content models also provide
interpretations of why certain games are liked by certain players for game
analytics purposes.
