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

Rishabh Joshi*, Varun Gupta, Xinyue Li, Yue Cui, Ziwen Wang, Yaser Norouzzadeh Ravari, Diego Klabjan, Rafet Sifa, Azita Parsaeian, Anders Drachen, Simon Demediuk

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.

Original languageEnglish (US)
Title of host publicationProceedings of the Australasian Computer Science Week Multiconference, ACSW 2019
PublisherAssociation for Computing Machinery
ISBN (Electronic)9781450366038
DOIs
StatePublished - Jan 29 2019
Event2019 Australasian Computer Science Week Multiconference, ACSW 2019 - Sydney, Australia
Duration: Jan 29 2019Jan 31 2019

Publication series

NameACM International Conference Proceeding Series

Conference

Conference2019 Australasian Computer Science Week Multiconference, ACSW 2019
CountryAustralia
CitySydney
Period1/29/191/31/19

Keywords

  • Clustering
  • Destiny
  • Player Profiling
  • Recommender Systems

ASJC Scopus subject areas

  • Software
  • Human-Computer Interaction
  • Computer Vision and Pattern Recognition
  • Computer Networks and Communications

Fingerprint Dive into the research topics of 'A Team Based Player Versus Player Recommender Systems Framework for Player Improvement'. Together they form a unique fingerprint.

  • Cite this

    Joshi, R., Gupta, V., Li, X., Cui, Y., Wang, Z., Ravari, Y. N., Klabjan, D., Sifa, R., Parsaeian, A., Drachen, A., & Demediuk, S. (2019). A Team Based Player Versus Player Recommender Systems Framework for Player Improvement. In Proceedings of the Australasian Computer Science Week Multiconference, ACSW 2019 [a45] (ACM International Conference Proceeding Series). Association for Computing Machinery. https://doi.org/10.1145/3290688.3290750