  Video-game players generate huge amounts of data, as everything they do
within a game is recorded. In particular, among all the stored actions and
behaviors, there is information on the in-game purchases of virtual products.
Such information is of critical importance in modern free-to-play titles, where
gamers can select or buy a profusion of items during the game in order to
progress and fully enjoy their experience.
  To try to maximize these kind of purchases, one can use a recommendation
system so as to present players with items that might be interesting for them.
Such systems can better achieve their goal by employing machine learning
algorithms that are able to predict the rating of an item or product by a
particular user. In this paper we evaluate and compare two of these algorithms,
an ensemble-based model (extremely randomized trees) and a deep neural network,
both of which are promising candidates for operational video-game recommender
engines.
  Item recommenders can help developers improve the game. But, more
importantly, it should be possible to integrate them into the game, so that
users automatically get personalized recommendations while playing. The
presented models are not only able to meet this challenge, providing accurate
predictions of the items that a particular player will find attractive, but
also sufficiently fast and robust to be used in operational settings.
