For years now, Valve has been testing new approaches to filter the glut of Steam games down to the ones in which individual users are most likely to show an interest. To that end, the company is today rolling out a machine-learning-powered “Interactive Recommender” trained on “billions of play sessions” from the Steam user base.
In the past, Steam has relied largely on crowd-sourced metadata like user-provided tags, user-curated lists, aggregate review scores, and sales data to drive its recommendation algorithms. But the new Interactive Recommender is different, Valve says, because it works without any initial internal or external information about the games themselves (save for the release date). “Instead, the model learns about the games for itself during the training process,” Valve says. “The model infers properties of games by learning what users do, not by looking at other extrinsic data.”
Your own playtime history is a core part of this neural-network-driven model. The number of hours you put into each game in your library is compared with that of millions of other Steam users so the neural network can make “informed suggestions” about the kinds of games you might like. “The idea is that if players with broadly similar play habits to you also tend to play another game you haven’t tried yet, then that game is likely to be a good recommendation for you,” Valve writes.