Title: Non-negative Tensor Factorization for Human Behavioral Pattern Mining in Online Games,
Abstract: 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.