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

doi:10.1145/3290688.3290750

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

Industrial Engineering and Management Sciences

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

5 Scopus citations

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 language	English (US)
Title of host publication	Proceedings of the Australasian Computer Science Week Multiconference, ACSW 2019
Publisher	Association for Computing Machinery
ISBN (Electronic)	9781450366038
DOIs	https://doi.org/10.1145/3290688.3290750
State	Published - Jan 29 2019
Event	2019 Australasian Computer Science Week Multiconference, ACSW 2019 - Sydney, Australia Duration: Jan 29 2019 → Jan 31 2019

Publication series

Name	ACM International Conference Proceeding Series

Conference

Conference	2019 Australasian Computer Science Week Multiconference, ACSW 2019
Country/Territory	Australia
City	Sydney
Period	1/29/19 → 1/31/19

Keywords

Clustering
Destiny
Player Profiling
Recommender Systems

ASJC Scopus subject areas

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

Access to Document

10.1145/3290688.3290750

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 Article a45 (ACM International Conference Proceeding Series). Association for Computing Machinery. https://doi.org/10.1145/3290688.3290750

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title = "A Team Based Player Versus Player Recommender Systems Framework for Player Improvement",

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.",

keywords = "Clustering, Destiny, Player Profiling, Recommender Systems",

author = "Rishabh Joshi and Varun Gupta and Xinyue Li and Yue Cui and Ziwen Wang and Ravari, {Yaser Norouzzadeh} and Diego Klabjan and Rafet Sifa and Azita Parsaeian and Anders Drachen and Simon Demediuk",

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year = "2019",

month = jan,

day = "29",

doi = "10.1145/3290688.3290750",

language = "English (US)",

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Joshi, R, Gupta, V, Li, X, Cui, Y, Wang, Z, Ravari, YN, 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, 2019 Australasian Computer Science Week Multiconference, ACSW 2019, Sydney, Australia, 1/29/19. https://doi.org/10.1145/3290688.3290750

A Team Based Player Versus Player Recommender Systems Framework for Player Improvement. / Joshi, Rishabh; Gupta, Varun; Li, Xinyue et al.
Proceedings of the Australasian Computer Science Week Multiconference, ACSW 2019. Association for Computing Machinery, 2019. a45 (ACM International Conference Proceeding Series).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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AU - Cui, Yue

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AU - Ravari, Yaser Norouzzadeh

AU - Klabjan, Diego

AU - Sifa, Rafet

AU - Parsaeian, Azita

AU - Drachen, Anders

AU - Demediuk, Simon

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AB - 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.

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ER -

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

Abstract

Publication series

Conference

Keywords

ASJC Scopus subject areas

Access to Document

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