Retention Prediction in Sandbox Games with Bipartite Tensor Factorization

Rafet Sifa; Michael Fedell; Nathan Franklin; Diego Klabjan; Shiva Ram; Arpan Venugopal; Simon Demediuk; Anders Drachen

doi:10.1007/978-3-030-52249-0_21

Retention Prediction in Sandbox Games with Bipartite Tensor Factorization

Rafet Sifa^*
, Michael Fedell
, Nathan Franklin
, Diego Klabjan
, Shiva Ram
, Arpan Venugopal
, Simon Demediuk
, Anders Drachen

^*Kontaktforfatter

SDU Metaverse Lab

Publikation: Kapitel i bog/rapport/konference-proceeding › Konferencebidrag i proceedings › Forskning › peer review

Abstract

Open world video games are designed to offer free-roaming virtual environments and agency to the players, providing a substantial degree of freedom to play the games in the way the individual player prefers. Open world games are typically either persistent, or for single-player versions semi-persistent, meaning that they can be played for long periods of time and generate substantial volumes and variety of user telemetry. Combined, these factors can make it challenging to develop insights about player behavior to inform design and live operations in open world games. Predicting the behavior of players is an important analytical tool for understanding how a game is being played and understand why players depart (churn). In this paper, we discuss a novel method of learning compressed temporal and behavioral features to predict players that are likely to churn or to continue engaging with the game. We have adopted the Relaxed Tensor Dual DEDICOM (RTDD) algorithm for bipartite tensor factorization of temporal and behavioral data, allowing for automatic representation learning and dimensionality reduction.

Originalsprog	Engelsk
Titel	Intelligent Computing - Proceedings of the 2020 Computing Conference
Redaktører	Kohei Arai, Supriya Kapoor, Rahul Bhatia
Antal sider	12
Forlag	Springer
Publikationsdato	2020
Sider	297-308
ISBN (Trykt)	9783030522483
DOI	https://doi.org/10.1007/978-3-030-52249-0_21
Status	Udgivet - 2020
Begivenhed	Science and Information Conference, SAI 2020 - London, Storbritannien Varighed: 16. jul. 2020 → 17. jul. 2020

Konference

Konference	Science and Information Conference, SAI 2020
Land/Område	Storbritannien
By	London
Periode	16/07/2020 → 17/07/2020

Navn	Advances in Intelligent Systems and Computing
Vol/bind	1228 AISC
ISSN	2194-5357

Bibliografisk note

Funding Information:
Part of this work was jointly funded by the Audience of the Future programme by UK Research and Innovation through the Industrial Strategy Challenge Fund (grant no.104775) and supported by the Digital Creativity Labs (digitalcreativity.ac.uk), a jointly funded project by EPSRC/AHRC/ Innovate UK under grant no. EP/M023265/1. Additionally, part of this research was funded by the Federal Ministry of Education and Research of Germany as part of the competence center for machine learning ML2R (01?S18038A).

Publisher Copyright:
© 2020, Springer Nature Switzerland AG.

Finansiering

Part of this work was jointly funded by the Audience of the Future programme by UK Research and Innovation through the Industrial Strategy Challenge Fund (grant no.104775) and supported by the Digital Creativity Labs (digitalcreativity.ac.uk), a jointly funded project by EPSRC/AHRC/ Innovate UK under grant no. EP/M023265/1. Additionally, part of this research was funded by the Federal Ministry of Education and Research of Germany as part of the competence center for machine learning ML2R (01?S18038A).

Dokumenter og links

10.1007/978-3-030-52249-0_21

SDU link

Tjek adgangen til materialet i Syddansk Universitetsbiblioteks søgemaskine

Citationsformater

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title = "Retention Prediction in Sandbox Games with Bipartite Tensor Factorization",

abstract = "Open world video games are designed to offer free-roaming virtual environments and agency to the players, providing a substantial degree of freedom to play the games in the way the individual player prefers. Open world games are typically either persistent, or for single-player versions semi-persistent, meaning that they can be played for long periods of time and generate substantial volumes and variety of user telemetry. Combined, these factors can make it challenging to develop insights about player behavior to inform design and live operations in open world games. Predicting the behavior of players is an important analytical tool for understanding how a game is being played and understand why players depart (churn). In this paper, we discuss a novel method of learning compressed temporal and behavioral features to predict players that are likely to churn or to continue engaging with the game. We have adopted the Relaxed Tensor Dual DEDICOM (RTDD) algorithm for bipartite tensor factorization of temporal and behavioral data, allowing for automatic representation learning and dimensionality reduction.",

keywords = "Behavioral analytics, Business intelligence, Tensor factorization",

author = "Rafet Sifa and Michael Fedell and Nathan Franklin and Diego Klabjan and Shiva Ram and Arpan Venugopal and Simon Demediuk and Anders Drachen",

note = "Publisher Copyright: {\textcopyright} 2020, Springer Nature Switzerland AG.; Science and Information Conference, SAI 2020 ; Conference date: 16-07-2020 Through 17-07-2020",

year = "2020",

doi = "10.1007/978-3-030-52249-0\_21",

language = "English",

isbn = "9783030522483",

series = "Advances in Intelligent Systems and Computing",

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Sifa, R, Fedell, M, Franklin, N, Klabjan, D, Ram, S, Venugopal, A, Demediuk, S & Drachen, A 2020, Retention Prediction in Sandbox Games with Bipartite Tensor Factorization. i K Arai, S Kapoor & R Bhatia (red), Intelligent Computing - Proceedings of the 2020 Computing Conference. Springer, Advances in Intelligent Systems and Computing, bind 1228 AISC, s. 297-308, Science and Information Conference, SAI 2020, London, Storbritannien, 16/07/2020. https://doi.org/10.1007/978-3-030-52249-0_21

Retention Prediction in Sandbox Games with Bipartite Tensor Factorization. / Sifa, Rafet; Fedell, Michael; Franklin, Nathan et al.
Intelligent Computing - Proceedings of the 2020 Computing Conference. red. / Kohei Arai; Supriya Kapoor; Rahul Bhatia. Springer, 2020. s. 297-308 (Advances in Intelligent Systems and Computing, Bind 1228 AISC).

Publikation: Kapitel i bog/rapport/konference-proceeding › Konferencebidrag i proceedings › Forskning › peer review

TY - GEN

T1 - Retention Prediction in Sandbox Games with Bipartite Tensor Factorization

AU - Sifa, Rafet

AU - Fedell, Michael

AU - Franklin, Nathan

AU - Klabjan, Diego

AU - Ram, Shiva

AU - Venugopal, Arpan

AU - Demediuk, Simon

AU - Drachen, Anders

PY - 2020

Y1 - 2020

N2 - Open world video games are designed to offer free-roaming virtual environments and agency to the players, providing a substantial degree of freedom to play the games in the way the individual player prefers. Open world games are typically either persistent, or for single-player versions semi-persistent, meaning that they can be played for long periods of time and generate substantial volumes and variety of user telemetry. Combined, these factors can make it challenging to develop insights about player behavior to inform design and live operations in open world games. Predicting the behavior of players is an important analytical tool for understanding how a game is being played and understand why players depart (churn). In this paper, we discuss a novel method of learning compressed temporal and behavioral features to predict players that are likely to churn or to continue engaging with the game. We have adopted the Relaxed Tensor Dual DEDICOM (RTDD) algorithm for bipartite tensor factorization of temporal and behavioral data, allowing for automatic representation learning and dimensionality reduction.

AB - Open world video games are designed to offer free-roaming virtual environments and agency to the players, providing a substantial degree of freedom to play the games in the way the individual player prefers. Open world games are typically either persistent, or for single-player versions semi-persistent, meaning that they can be played for long periods of time and generate substantial volumes and variety of user telemetry. Combined, these factors can make it challenging to develop insights about player behavior to inform design and live operations in open world games. Predicting the behavior of players is an important analytical tool for understanding how a game is being played and understand why players depart (churn). In this paper, we discuss a novel method of learning compressed temporal and behavioral features to predict players that are likely to churn or to continue engaging with the game. We have adopted the Relaxed Tensor Dual DEDICOM (RTDD) algorithm for bipartite tensor factorization of temporal and behavioral data, allowing for automatic representation learning and dimensionality reduction.

KW - Behavioral analytics

KW - Business intelligence

KW - Tensor factorization

U2 - 10.1007/978-3-030-52249-0_21

DO - 10.1007/978-3-030-52249-0_21

M3 - Article in proceedings

AN - SCOPUS:85088520279

SN - 9783030522483

T3 - Advances in Intelligent Systems and Computing

SP - 297

EP - 308

BT - Intelligent Computing - Proceedings of the 2020 Computing Conference

A2 - Arai, Kohei

A2 - Kapoor, Supriya

A2 - Bhatia, Rahul

PB - Springer

T2 - Science and Information Conference, SAI 2020

Y2 - 16 July 2020 through 17 July 2020

ER -

Sifa R, Fedell M, Franklin N, Klabjan D, Ram S, Venugopal A et al. Retention Prediction in Sandbox Games with Bipartite Tensor Factorization. I Arai K, Kapoor S, Bhatia R, red., Intelligent Computing - Proceedings of the 2020 Computing Conference. Springer. 2020. s. 297-308. (Advances in Intelligent Systems and Computing, Bind 1228 AISC). doi: 10.1007/978-3-030-52249-0_21

Retention Prediction in Sandbox Games with Bipartite Tensor Factorization

Abstract

Konference

Bibliografisk note

Finansiering

Dokumenter og links

SDU link

Fingeraftryk

Citationsformater