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

*Corresponding author for this work

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

2 Scopus citations


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.

Original languageEnglish (US)
Title of host publicationIntelligent Computing - Proceedings of the 2020 Computing Conference
EditorsKohei Arai, Supriya Kapoor, Rahul Bhatia
Number of pages12
ISBN (Print)9783030522483
StatePublished - 2020
EventScience and Information Conference, SAI 2020 - London, United Kingdom
Duration: Jul 16 2020Jul 17 2020

Publication series

NameAdvances in Intelligent Systems and Computing
Volume1228 AISC
ISSN (Print)2194-5357
ISSN (Electronic)2194-5365


ConferenceScience and Information Conference, SAI 2020
Country/TerritoryUnited Kingdom


  • Behavioral analytics
  • Business intelligence
  • Tensor factorization

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Computer Science(all)


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