From Theory to Behaviour: Towards a General Model of Engagement

Engagement is a fuzzy concept. In the present work we operationalize engagement mechanistically by linking it directly to human behaviour and show that the construct of engagement can be used for shaping and interpreting data-driven methods. First we outline a formal framework for engagement modelling. Second we expanded on our previous work on theory-inspired data-driven approaches to better model the engagement process by proposing a new modelling technique, the Melchoir Model. Third, we illustrate how, through model comparison and inspection, we can link machine-learned models and underlying theoretical frameworks. Finally we discuss our results in light of a theory-driven hypothesis and highlight potential application of our work in industry.

[1]  Kyung-Joong Kim,et al.  Game Data Mining Competition on Churn Prediction and Survival Analysis Using Commercial Game Log Data , 2018, IEEE Transactions on Games.

[2]  Gaël Varoquaux,et al.  Scikit-learn: Machine Learning in Python , 2011, J. Mach. Learn. Res..

[3]  Na Wang,et al.  Micro- and macro-level churn analysis of large-scale mobile games , 2019, Knowledge and Information Systems.

[4]  Ameet Talwalkar,et al.  Hyperband: A Novel Bandit-Based Approach to Hyperparameter Optimization , 2016, J. Mach. Learn. Res..

[5]  M. Boguñá,et al.  Quantifying Human Engagement into Playful Activities , 2020, Scientific Reports.

[6]  Diego Klabjan,et al.  Rapid Prediction of Player Retention in Free-to-Play Mobile Games , 2016, AIIDE.

[7]  Julian Togelius,et al.  Like a DNA String: Sequence-Based Player Profiling in Tom Clancy's The Division , 2018, AIIDE.

[8]  K. Berridge,et al.  What is the role of dopamine in reward: hedonic impact, reward learning, or incentive salience? , 1998, Brain Research Reviews.

[9]  Christian Bauckhage,et al.  How players lose interest in playing a game: An empirical study based on distributions of total playing times , 2012, 2012 IEEE Conference on Computational Intelligence and Games (CIG).

[10]  Guigang Zhang,et al.  Deep Learning , 2016, Int. J. Semantic Comput..

[11]  D. Sculley,et al.  Web-scale k-means clustering , 2010, WWW '10.

[12]  L. Jennings,et al.  Guns , 1976 .

[13]  Magy Seif El-Nasr,et al.  Game Analytics , 2013, Springer London.

[14]  Alex R. Wade,et al.  Modelling Early User-Game Interactions for Joint Estimation of Survival Time and Churn Probability , 2019, 2019 IEEE Conference on Games (CoG).

[15]  Paolo Burelli,et al.  Combining Sequential and Aggregated Data for Churn Prediction in Casual Freemium Games , 2019, 2019 IEEE Conference on Games (CoG).

[16]  Elaine Toms,et al.  What is user engagement? A conceptual framework for defining user engagement with technology , 2008, J. Assoc. Inf. Sci. Technol..

[17]  Daniele Loiacono,et al.  Player Modeling , 2013, Artificial and Computational Intelligence in Games.

[18]  Leland McInnes,et al.  UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction , 2018, ArXiv.

[19]  Ana Fernández del Río,et al.  A Time Series Approach To Player Churn and Conversion in Videogames , 2020, Intell. Data Anal..

[20]  W. Marsden.  I and J , 2012 .

[21]  Georgios N. Yannakakis,et al.  Player modeling using self-organization in Tomb Raider: Underworld , 2009, 2009 IEEE Symposium on Computational Intelligence and Games.

[22]  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).

[23]  Ana Fernández del Río,et al.  Profiling Players with Engagement Predictions , 2019, 2019 IEEE Conference on Games (CoG).

[24]  Samuel M. McClure,et al.  A computational substrate for incentive salience , 2003, Trends in Neurosciences.

[25]  G. G. Stokes.  "J." , 1890, The New Yale Book of Quotations.

[26]  James M. Boyle,et al.  Engagement in digital entertainment games: A systematic review , 2012, Comput. Hum. Behav..