Sequence alignment based analysis of player behavior in massively multiplayer online role-playing games (MMORPGs)

Kyong Jin Shim, Jaideep Srivastava

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

3 Citations (Scopus)

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.

Original languageEnglish
Title of host publicationProceedings - 10th IEEE International Conference on Data Mining Workshops, ICDMW 2010
Pages997-1004
Number of pages8
DOIs
Publication statusPublished - 1 Dec 2010
Event10th IEEE International Conference on Data Mining Workshops, ICDMW 2010 - Sydney, NSW, Australia
Duration: 14 Dec 201017 Dec 2010

Publication series

NameProceedings - IEEE International Conference on Data Mining, ICDM
ISSN (Print)1550-4786

Other

Other10th IEEE International Conference on Data Mining Workshops, ICDMW 2010
CountryAustralia
CitySydney, NSW
Period14/12/1017/12/10

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Keywords

  • Games
  • Inactivity
  • Player behavior
  • Sequence alignment
  • User behavior

ASJC Scopus subject areas

  • Engineering(all)

Cite this

Shim, K. J., & Srivastava, J. (2010). Sequence alignment based analysis of player behavior in massively multiplayer online role-playing games (MMORPGs). In Proceedings - 10th IEEE International Conference on Data Mining Workshops, ICDMW 2010 (pp. 997-1004). [5693404] (Proceedings - IEEE International Conference on Data Mining, ICDM). https://doi.org/10.1109/ICDMW.2010.166