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A Team Based Player Versus Player Recommender Systems Framework For Player Improvement

 
: Joshi, R.; Gupta, V.; Li, X.Y.; Cui, Y.; Wang, Z.W.; Ravari, Y.N.; Klabjan, D.; Sifa, R.; Parsaeian, A.; Drachen, A.; Demediuk, S.

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Fulltext ()

Association for Computing Machinery -ACM-:
ACSW 2019, Proceedings of the Australasian Computer Science Week Multiconference
New York: ACM, 2019
ISBN: 978-1-4503-6603-8
Art. 45, 7 pp.
Australasian Computer Science Week Multiconference (ACSW) <2019, Sydney>
English
Conference Paper, Electronic Publication
Fraunhofer IAIS ()

Abstract
Modern Massively Multi-player Online Games (MMOGs) have grown to become extremely complex in terms of the usable resources in the games, resulting in an increase in the amount of data collected by tracking the in-game activities of players. This has opened the door for researchers to come up with novel methods to utilize this data to improve and personalize the user experience. In this paper, a novel but flexible framework towards building a team based recommender system for player-versus-player (PvP) content in such MMOGs is presented, and applied to a case study in the context of the major commercial title Destiny 2. The framework combines behavioral profiling via cluster analysis with recommendation systems to look at teams of players as a unit, as well as the individual players, to make recommendations to the players, with the purpose of providing information to them towards improving their performance.

: http://publica.fraunhofer.de/documents/N-569174.html