Predicting Retention in Sandbox Games with Tensor Factorization-based Representation Learning

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 seminonnegative constrained method. These approaches are validated against a detailed behavioral data set from the AAA open-world game Just Cause 2.

[1]  Alla Rozovskaya,et al.  Using DEDICOM for Completely Unsupervised Part-of-Speech Tagging , 2009 .

[2]  Sam Devlin,et al.  Predicting player disengagement and first purchase with event-frequency based data representation , 2015, 2015 IEEE Conference on Computational Intelligence and Games (CIG).

[3]  Christian Bauckhage,et al.  Beyond heatmaps: Spatio-temporal clustering using behavior-based partitioning of game levels , 2014, 2014 IEEE Conference on Computational Intelligence and Games.

[4]  Tamara G. Kolda,et al.  Temporal Analysis of Semantic Graphs Using ASALSAN , 2007, Seventh IEEE International Conference on Data Mining (ICDM 2007).

[5]  Henk A. L. Kiers.  Desicom: Decomposition of Asymmetric Relationships Data into Simple Components , 1997 .

[6]  Peng Gao,et al.  Churn prediction for high-value players in casual social games , 2014, 2014 IEEE Conference on Computational Intelligence and Games.

[7]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[8]  Arnav Jhala,et al.  Modeling Player Retention in Madden NFL 11 , 2011, IAAI.

[9]  Christian Bauckhage,et al.  User Churn Migration Analysis with DEDICOM , 2015, RecSys.

[10]  Christian Bauckhage,et al.  k-Maxoids Clustering , 2015, LWA.

[11]  J. Berge,et al.  Computational solutions for the problem of negative saliences and nonsymmetry in INDSCAL , 1993 .

[12]  Hans-Peter Kriegel,et al.  A Three-Way Model for Collective Learning on Multi-Relational Data , 2011, ICML.

[13]  Ruck Thawonmas,et al.  Analysis of revisitations in online games , 2011, Entertain. Comput..

[14]  Christian Bauckhage,et al.  Archetypical motion: Supervised game behavior learning with Archetypal Analysis , 2013, 2013 IEEE Conference on Computational Inteligence in Games (CIG).

[15]  Christian Bauckhage,et al.  Predicting Purchase Decisions in Mobile Free-to-Play Games , 2015, AIIDE.

[16]  Christian Bauckhage,et al.  Predicting player churn in the wild , 2014, 2014 IEEE Conference on Computational Intelligence and Games.

[17]  Christian Bauckhage,et al.  Guns, swords and data: Clustering of player behavior in computer games in the wild , 2012, 2012 IEEE Conference on Computational Intelligence and Games (CIG).

[18]  J. Chang,et al.  Analysis of individual differences in multidimensional scaling via an n-way generalization of “Eckart-Young” decomposition , 1970 .