Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

12-2010

Abstract

This study proposes a sequence alignment-based behavior analysis framework (SABAF) developed for predicting inactive game players that either leave the game permanently or stop playing the game for a long period of time. Sequence similarity scores and derived statistics form profile databases of inactive players and active players from the past. SABAF uses global and local sequence alignment algorithms and a unique scoring scheme to measure similarity between activity sequences. SABAF is tested on the game player activity data of Ever Quest II, a popular massively multiplayer online role-playing game developed by Sony Online Entertainment. SABAF consists of the following key components: 1) sequence alignment-based player profile databases, 2) feature selection schemes and prediction model building, and 3) decision support model for determining inactive players.

Keywords

User behavior, games, inactivity, player behavior, sequence alignment

Discipline

Databases and Information Systems | Numerical Analysis and Scientific Computing

Research Areas

Data Science and Engineering

Publication

IEEE International Conference on Data Mining Workshops: ICDMW 2010, Sydney, 14 December: Proceedings

First Page

997

Last Page

1004

ISBN

9780769542577

Identifier

10.1109/ICDMW.2010.166

Publisher

IEEE Computer Society

City or Country

Los Alamitos, CA

Copyright Owner and License

Publisher

Additional URL

https://doi.org/10.1109/ICDMW.2010.166

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