Recommendation Applications and Systems at Electronic Arts

The digital game industry has recently adopted recommendation systems to provide suitable game and content choices to players. Recommendations in digital games have several unique applications and challenges compared to other well known recommendation system such as those for movies and books. Designers must adopt different architectures and algorithms to overcome these challenges. In this talk, we describe the game recommendation system at Electronic Arts. It leverages heterogeneous player data across many games to provide intelligent recommendations. We discuss three example applications: recommending games for purchase, suitable game map, and game difficulty. Like the movie and book recommendation problem, one application is to recommend the next favorite games for a player. Digital games fall into a wide range of genres such as first player shooting (FPS), sports, and role-playing games (RPG). Games within the same genre however tend to be unique and creative. While the recommendation item space is smaller, the recommendation system should also manage different types of contents such as games and extra downloadable contents to play, editorial videos and tutorials to watch. The second application provides the game mode and map recommendations within a game to improve player experience. Many online digital games, especially FPS and sports games, contain different maps and game modes to provide diverse gameplay experience. Different maps and game modes often require different skill levels, strategies, or cooperation from players, and the maps and game modes are often played repeatedly. Therefore, recommending the most suitable map and game mode is important from smooth onboarding experience to retain players who are likely to churn. In the map and game mode recommendation application, the algorithms need to evaluate both the short-term actions as well as long term effects of playing different maps and game modes to optimize player's engagement. In addition, we also use the same recommendation system to adjust in-game configurations such as difficulty. Players have a wide variety of experiences, skills, learning rates, and playing styles, and will react differently to the same difficulty setting. Second, even for an individual player, one's difficulty preference may also change over time. For example, in a level progression game, a player who loses the first several attempts to one level might feel much less frustrated compared to losing after tens of unsuccessful trials. The difficulty recommendation provides suggestions and adjustments on game configuration based on the player's previous gameplay experience to maximize the engagement. For online multiplayer games, recommending partners and opponents in matchmaking is also an effective way to improve player experience. We developed one flexible recommendation system to satisfy the need of different applications and that executes data-driven algorithms such collaborative filtering and multi-armed bandit. The centralized system leverages entire player and game data for all recommendation applications in digital games, supports unified roll-out and update, and at the same time measures the performance together via A/B testing experiments. Moreover, the one system strategy is easy to generate consistent recommendations across multiple games and platforms. We tested these recommendation applications in EA website and games, an observed significant improvements in click-through-rate and engagement.