Player Profiling: GUR Summit 2015

Introduction and Scope

This document contains a brief reading list accompanying the GUR SIG Summit 2015 presentation “user profiling”.

There is a wealth of literature on profiling, machine learning, data mining and cluster analysis in particular, but only a limited amount of research on the application of these domains to games specifically. This is rapidly changing thanks to the popularity of game AI and game analytics as research domains, however. One problem with much of the literature is that it is located behind the paywalls of academic publishing. Some material can be found via pre-prints in open-source repositories like arxiv, or via scientist social networks like ResearchGate and Academia.edu. Additional information is available via the personal websites of the authors.

Some references on this list are behind paywalls of academic publishers. However, they can usually be accessed at any university library, or the websites of the authors.

GoogleScholar is a good search engine for finding research papers and seeing if they are freely available.

 User Profiling

  • Fawcett, T. and Provost, F. (1996): Combining Data Mining and Machine Learning for Effective User Profiling. Proceedings of KDD-96, AAAI publishers. DOI:http://dx.doi.org/10.1609/aimag.v18i2.1292
  • Krulwich, B. (2015): Lifestyle Finder: Intelligent User Profiling Using Large-Scale Demographic Data. AI Magazine, vol. 18 (2), AAAI Publishers.
  • Elmer, G. (2004). “Profiling Machines. Mapping the Personal Information Economy”. MIT Press.
  • Hildebrandt, Mireille; Gutwirth, Serge (2008). Profiling the European Citizen. Cross Disciplinary Perspectives. Springer, Dordrecht. DOI: 10.1007/978-1-4020-6914-7. ISBN 978-1-4020-6913-0.
  • Middleton, S. (2004): Ontological user profiling in recommender systems. ACM Transactions on Information Systems, vol 22 issue 1. DOI: 1145/963770.963773

Play personas

  • Canossa, A. & Drachen, A. (2009): Patterns of Play: Play-Personas in User-Centered Game Development. In Proceedings of the Digital Games Research Association Conference: Breaking New Ground: Innovation in Games, Play, Practice and Theory, DIGRA Digital Library: http://www.digra.org/dl/display_html?chid=http://www.digra.org/dl/db/09287.49165.pdf
  • Canossa, A. & Drachen, A. (2009): Play-Personas: Behaviors and Belief systems in User-Centered Game Design. In Proceedings of INTERACT, LNCS Vol. 5727, 510-523. DOI: 10.1007/978-3-642-03658-3_55.
  • Tychsen, A., Canossa, A.: Defining Personas in Games Using Metrics. In: Proceedings of Future Play 2008, pp. 73-80 (2008)
  • Bennett, A.: Personas: Practice and Theory, in Design Studies: Theory and Research in Graphic Design, Princeton Architectural Press (2006)

Cohort analysis

Segmentation

Affinity Mining

  • Han, J. Kamber M. and Pei Jian (2011): Data Mining: Concepts and Techiques, 3rd edition, Morgan Kaufmann.ISBN: 978-0123814791
  • M. J. (2011) Scalable algorithms for association mining. Knowledge and Data Engineering journal, vol. 12 issue 3. IEEE Publishers.
  • Ceglar, Aa. And Roddick (2006) Association mining. ACM Computing Surveys, volume 38, number 2, article no. 5. doi>1145/1132956.1132958

Cluster Analysis

  • Data Clustering: Algorithms and Applications. C. Aggarwal and C. Reddy, Eds., Chapman & Hall/CRC, 2013. A good introduction to clustering.
  • Information Theory, Inference, and Learning Algorithms, D. MacKay, Cambridge University Press, 2003. A good all-round introduction to clustering.
  • Data Mining: Concepts and Techniques. Han, J. and Kamber, M. Morgan Kaufmann Publishers, 2007. A good all-round introduction to data mining including clustering.
  • Why So Many Clustering Algorithms: A Position Paper, V. Estivill-Castro, ACM SIGKDD Explorations Newsletter, vol. 4, no. 1, 2002. Explains the organization of clustering algorithms and why there are many different models.

Cluster Analysis Applied in Games

These are references specifically on cluster analysis in the context of digital games. The list here only includes a few examples, but the bibliographies in each of them provides numerous links to additional material.

  • Introducing Clustering I-IV. Available from blog.gameanalytics.com or gamasutra.com
  • Clustering Game Behavior Data. In the IEEE Transactions of Computational Intelligence and AI in Games. Bauckhage, C. and Drachen, A.
  • Guns, Swords, and Data: Clustering of Player Behavior in Computer Games in the Wild, in Proceedings of the Computational Intelligence in Games Conference. Drachen, R. Sifa, C. Bauckhage, and C. Thurau. URL: andersdrachen.com/publications/
  • Analyzing Auctions: The Case of Glitch. Drachen, A.; Baskin, S.; Riley J. and Klabjan, D. Available online. URL: http://www.gamasutra.com/blogs/AndersDrachen/20140326/213907/Analysing_Auctions_The_Case_of_Glitch.php
  • Visualizing Dynamic Behavior Flow. Drachen, A.; Baskin, S.; Riley J. and Klabjan, D. Available online. URL: http://www.gamasutra.com/blogs/AndersDrachen/20140410/215252/Visualizing_Dynamic_Behavior_Flow.php
  • Player Modeling using Self-Organization in Tomb Raider: Underworld. Drachen, A.; Yannakakis, G. and Canossa, A. In Proceedings of IEEE Computational Intelligence in Games, 2009. URL: andersdrachen.com/publications/
  • Evolutionary Behavioral Clustering in Tomb Raider. In Proceedings of IEEE Computational Intelligence in Games 2013. Sifa, R.; Drachen, A. & Bauckhage, C. URL: andersdrachen.com/publications/
  • World of Warcraft Avatar History Dataset, Y.-T. Lee, K.-T. Chen, Y.-M. Cheng, and C.-L. Lei, in Proceedings of Multimedia Systems, 2001.
  • Visualization of Online-game Players Based on Their Action Behaviors, Thawonmas and K. Iizuka, International Journal of Computer Games Technology, 2008.
  • Analyzing the Evolution of Social Groups in World of Warcraft, Thurau and C. Bauckhage, in Proceedings of the IEEE Computational Intelligence in Games, 2010.
  • Behavioral Profiles of Character Types in EverQuest II, Shim and J. Srivastava, in Proceedings of the IEEE Computational Intelligence in Games Conference, 2010.
  • Player Modeling for Intelligent Difficulty Adjustment, Missura and T. G¨artner, in Proceedings of Discovery Science Conference, 2009.
  • A Data Mining Approach to Strategy Prediction, B. Weber and M. Mateas, in Proceedings of the IEEE Computational Intelligence in Games Conference, 2009.
  • Real-time game adaptation for optimizing player satisfaction, G. Yannakakis and J. Hallam, IEEE Trans. Computational Intelligence and AI in Games, vol. 1, no. 2, 2009.
  • Archetypical Motion: Supervised Game Behavior Learning with Archetypal Analysis, R. Sifa and C. Bauckhage, in Proceedings of the IEEE Computational Intelligence in Games Conference, 201309.

 

Books on Game Analytics

There are currently (at least) five books on game analytics, or very closely related to it/mentioning it, on the market. These can be supplemented by a few texts on business intelligence and analytics in general, as well as books on data mining.

 

Game Analytics: Maximizing the Value of Player Data 

Edited by El-Nasr, Drachen and Canossa, this 800-page mammoth covers a variety of topics in analytics, with a focus on behavioral telemetry and its role in game development and research. Aimed at both beginners and experts, and authored by more than 50 experts from industry and research, it covers many important bases such as game data mining, visualization, monetization and user research, as well as topics such as metrics for learning games and quantitative user testing. The sheer scope of the book means that everyone will find something of interest inside, but it should be noted that the book is aimed at providing information and coverage rather than a how-to volume. (disclaimer: I am an editor on this book and therefore horribly biased).

Social Game Design: Monetization Methods and Mechanics

Authored by the experienced developers Tim Fields and Brandon Cotton this book focuses on the design and business side of social game development, and outlines what makes games compelling and why people will pay to play them. The book handily outlines different business models, player acquisition strategies, analytics strategies and retention considerations. Recommended for both beginners and experts in analytics who work with social/online games. An excerpt available on Gamasutra.com.

Freemium Economics: Leveraging Analytics and User Segmentation to Drive Revenue

Your freemium product generates vast volumes of data, but using that data to maximize conversion, boost retention, and deliver revenue can be challenging if you don’t fully understand the impact that small changes can have on revenue. In this book, author Eric Seufert provides clear guidelines for using data and analytics through all stages of development to optimize your implementation of the freemium model. Freemium Economics de-mystifies the freemium model through an exploration of its core, data-oriented tenets, so that you can apply it methodically rather than hoping that conversion and revenue will naturally follow product launch. Written by Eric Seufert.

Free 2 Play: Making Money From Games You Give Away

This book covers a wide range of topics from psychology of players, game design techniques, and an introduction to basic game analytics practices. By Will Luton.

The Curve: How Smart Companies Find High-Value Customers

In The Curve, Nicholas Lovell weaves together stories from disparate industries to show how smart companies are solving this puzzle. From video games to pop music to model trains, the Internet helps businesses forge direct relationships with a vast global audience by building communities and offering bespoke products and experiences. By Nicholas Lovell.

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