Non-negative Tensor Factorization for Human Behavioral Pattern Mining in Online Games
This work addresses the challenge of analyzing human interactions in online games for researchers, though it is incremental as it applies an existing method to a new domain.
The authors tackled the problem of extracting interpretable behavioral patterns from multiplayer online game data by applying Non-negative Tensor Factorization to League of Legends match histories, resulting in the separation of players into groups with similar strategies and temporal trajectories.
Multiplayer online battle arena has become a popular game genre. It also received increasing attention from our research community because they provide a wealth of information about human interactions and behaviors. A major problem is extracting meaningful patterns of activity from this type of data, in a way that is also easy to interpret. Here, we propose to exploit tensor decomposition techniques, and in particular Non-negative Tensor Factorization, to discover hidden correlated behavioral patterns of play in a popular game: League of Legends. We first collect the entire gaming history of a group of about one thousand players, totaling roughly $100K$ matches. By applying our methodological framework, we then separate players into groups that exhibit similar features and playing strategies, as well as similar temporal trajectories, i.e., behavioral progressions over the course of their gaming history: this will allow us to investigate how players learn and improve their skills.