2017/02/19 by Anna Sapienza, Alessandro Bessi, Sapienza, Anna +3
Mathematics · #FOS: Computer and information sciences #FOS: Physical sciences #Human-Computer Interaction (cs.HC) #Machine Learning (cs.LG) #Physics and Society (physics.soc-ph) #Social and Information Networks (cs.SI) #Tensor decomposition and applications
paper · pdf · doi:10.48550/arxiv.1702.05695
openalex publication_date 2017/02/19 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.