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Large-scale cross-game player behavior analysis on steam

: Sifa, Rafet; Drachen, Anders; Bauckhage, Christian

Volltext (PDF; )

Jhala, A. ; Association for the Advancement of Artificial Intelligence -AAAI-:
Eleventh AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment, AIIDE 2015. Proceedings : 14-18 November 2015, University of California, Santa Cruz, CA, USA
Menlo Park: AAAI Press, 2015
ISBN: 978-1-57735-740-7
Conference on Artificial Intelligence and Interactive Digital Entertainment (AIIDE) <11, 2015, Santa Cruz/Calif.>
Konferenzbeitrag, Elektronische Publikation
Fraunhofer IAIS ()

Behavioral game analytics has predominantly been confined to work on single games, which means that the cross-game applicability of current knowledge remains largely unknown. Here four experiments are presented focusing on the relationship between game ownership, time invested in playing games, and the players themselves, across more than 3000 games distributed by the Steam platform and over 6 million players, covering a total playtime of over 5 billion hours. Experiments are targeted at uncovering high-level patterns in the behavior of players focusing on playtime, using frequent itemset mining on game ownership, cluster analysis to develop playtime-dependent player profiles, correlation between user game rankings and, review scores, playtime and game ownership, as well as cluster analysis on Steam games. Within the context of playtime, the analyses presented provide unique insights into the behavior of game players as they occur across games, for example in how players distribute their time across games.