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  4. Predicting retention in sandbox games with tensor factorization-based representation learning
 
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2016
  • Konferenzbeitrag

Titel

Predicting retention in sandbox games with tensor factorization-based representation learning

Abstract
Major commercial (AAA) games increasingly transit to a semi-persistent or persistent format in order to extend the value of the game to the player, and to add new sources of revenue beyond basic retail sales. Given this shift in the design of AAA titles, game analytics needs to address new types of problems, notably the problem of forecasting future player behavior. This is because player retention is a key factor in driving revenue in semi-persistent titles, for example via downloadable content. This paper introduces a model for predicting retention of players in AAA games and provides a tensor-based spatio-temporal model for analyzing player trajectories in 3D games. We show how knowledge as to trajectories can help with predicting player retention. Furthermore, we describe two new algorithms for three way DEDICOM including a fast gradient method and a semi-nonnegative constrained method. These approaches are validated against a detailed behavioral data set from the AAA open-world game Just Cause 2.
Author(s)
Sifa, Rafet
Fraunhofer-Institut für Intelligente Analyse- und Informationssysteme IAIS
Srikanth, Sridev
Uni Bonn
Drachen, Anders
Ojeda, César
Fraunhofer-Institut für Intelligente Analyse- und Informationssysteme IAIS
Bauckhage, Christian
Fraunhofer-Institut für Intelligente Analyse- und Informationssysteme IAIS
Hauptwerk
IEEE Conference on Computational Intelligence and Games, CIG 2016
Konferenz
Conference on Computational Intelligence and Games (CIG) 2016
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DOI
10.1109/CIG.2016.7860405
Language
Englisch
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IAIS
Tags
  • game

  • matrix decomposition

  • predictive model

  • feature extraction

  • analytical model

  • trajectory

  • tensile stress

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