Exploiting limited players’ behavioral data to predict churn in gamification
Tài liệu tham khảo
Ahmad, 2017, Trees vs neurons: Comparison between random forest and ann for high-resolution prediction of building energy consumption, Energy and Buildings, 147, 77, 10.1016/j.enbuild.2017.04.038
Arnaiz-González, 2016, Fusion of instance selection methods in regression tasks, Information Fusion, 30, 69, 10.1016/j.inffus.2015.12.002
Batista, 2004, A study of the behavior of several methods for balancing machine learning training data, ACM SIGKDD Explorations Newsletter, 6, 20, 10.1145/1007730.1007735
Bertens, 2017, Games and big data: A scalable multi-dimensional churn prediction model, 33
Branco, 2016, A survey of predictive modeling on imbalanced domains, ACM Computing Surveys (CSUR), 49, 1, 10.1145/2907070
Branco, 2017, Smogn: a pre-processing approach for imbalanced regression, Machine Learning Research, 36
Brave, 2003, Emotion in human-computer interaction, The human-computer interaction handbook: fundamentals, evolving technologies and emerging applications, 81
Caillois, 2001
Chawla, 2002, Smote: synthetic minority over-sampling technique, Journal of Artificial Intelligence Research, 16, 321, 10.1613/jair.953
Coussement, 2008, Churn prediction in subscription services: An application of support vector machines while comparing two parameter-selection techniques, Expert Systems with Applications, 34, 313, 10.1016/j.eswa.2006.09.038
Crone, 2012, Instance sampling in credit scoring: An empirical study of sample size and balancing, International Journal of Forecasting, 28, 224, 10.1016/j.ijforecast.2011.07.006
Csikszentmihalyi, 1992
Datta, 2000, Automated cellular modeling and prediction on a large scale, Artificial Intelligence Review, 14, 485, 10.1023/A:1006643109702
Deci, 2000, The what and why of goal pursuits: Human needs and the self-determination of behavior, Psychological Inquiry, 11, 227, 10.1207/S15327965PLI1104_01
Drachen, 2016, Rapid prediction of player retention in free-to-play mobile games
Edwards, 2016, Gamification for health promotion: systematic review of behaviour change techniques in smartphone apps, BMJ Open, 6, 10.1136/bmjopen-2016-012447
El-Nasr, 2016
Ferreira, 2016, What is survival analysis, and when should i use it?, Jornal Brasileiro de Pneumologia, 42, 77, 10.1590/S1806-37562016000000013
Ferron, 2019, Play&go, an urban game promoting behaviour change for sustainable mobility, Interaction Design and Architecture(s) Journal, 24
Fogg, 2002, Persuasive technology: using computers to change what we think and do, Ubiquity, 2002, 5, 10.1145/764008.763957
Friedman, 2001, Greedy function approximation: a gradient boosting machine, Annals of Statistics, 1189
Fritz, T., Huang, E.M., Murphy, G.C., Zimmermann, T., 2014. Persuasive technology in the real world: a study of long-term use of activity sensing devices for fitness, in: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, ACM. pp. 487–496.
Hadden, J., Tiwari, A., Roy, R., Ruta, D., 2006. Churn prediction using complaints data, in: World Academy of Science, Engineering and Technology.
Hadiji, 2014, Predicting player churn in the wild, 1
Haixiang, 2017, Learning from class-imbalanced data: Review of methods and applications, Expert Systems with Applications, 73, 220, 10.1016/j.eswa.2016.12.035
Hamari, 2013, Transforming homo economicus into homo ludens: A field experiment on gamification in a utilitarian peer-to-peer trading service, Electronic Commerce Research and Applications, 12, 236, 10.1016/j.elerap.2013.01.004
Hooshyar, 2018, Data-driven approaches to game player modeling: a systematic literature review, CSUR, 50, 90, 10.1145/3145814
Hsiao, 2016, What drives in-app purchase intention for mobile games? an examination of perceived values and loyalty, Electronic Commerce Research and Applications, 16, 18, 10.1016/j.elerap.2016.01.001
Huotari, 2017, A definition for gamification: anchoring gamification in the service marketing literature, Electronic Markets, 27, 21, 10.1007/s12525-015-0212-z
IJsselsteijn, W., De Kort, Y., Poels, K., 2013. The game experience questionnaire. Eindhoven: Technische Universiteit Eindhoven, 3–9.
Isbister, 2008
Japkowicz, 2002, The class imbalance problem: A systematic study, Intelligent Data Analysis, 6, 429, 10.3233/IDA-2002-6504
Jennett, 2008, Measuring and defining the experience of immersion in games, International Journal of Human-Computer Studies, 66, 641, 10.1016/j.ijhcs.2008.04.004
Kawale, 2009, Churn prediction in mmorpgs: A social influence based approach, 423
Kiili, 2018, Evaluating the effectiveness of a game-based rational number training-in-game metrics as learning indicators, Computers & Education, 120, 13, 10.1016/j.compedu.2018.01.012
Kim, 2008, Tracking real-time user experience (true): a comprehensive instrumentation solution for complex systems, 443
Kim, 2017, Churn prediction of mobile and online casual games using play log data, PloS One, 12, 1
Koivisto, 2019, The rise of motivational information systems: A review of gamification research, International Journal of Information Management, 45, 191, 10.1016/j.ijinfomgt.2018.10.013
Krawczyk, 2016, Learning from imbalanced data: open challenges and future directions, Progress in Artificial Intelligence, 5, 221, 10.1007/s13748-016-0094-0
Lee, 2016, Predicting churn in mobile free-to-play games, 1046
Legaki, 2019
Liu, 2002, On issues of instance selection, Data Mining and Knowledge Discovery, 6, 115, 10.1023/A:1014056429969
Liu, 2018, A semi-supervised and inductive embedding model for churn prediction of large-scale mobile games, 277
Liu, 2019, Micro-and macro-level churn analysis of large-scale mobile games, Knowledge and Information Systems, 1
Loh, 2015, Serious games analytics: Theoretical framework, Serious Games Analytics. Springer, 3, 10.1007/978-3-319-05834-4_1
Loria, E., Marconi, A., 2018. Player types and player behaviors: Analyzing correlations in an on-the-field gamified system, in: CHIPlay’18 Extended Abstracts, ACM. pp. 531–538.
Loria, 2019, Sustainable mobility in smart cities: the key role of gamified motivational systems for citizens’ engagement and behavior change
Loria, E., Paissan, F., Marconi, A., 2019. Exploiting General-Purpose In-Game Behaviours to Predict Players Churn in Gameful Systems, in: Proceedings of The First Game Analytics Workshop, CEUR Workshop Proceedings, Atlanta, GA, USA.
Mahlmann, 2010, Predicting player behavior in tomb raider: Underworld, 178
Mandryk, 2006, A continuous and objective evaluation of emotional experience with interactive play environments, 1027
Mandryk, 2006, Using psychophysiological techniques to measure user experience with entertainment technologies, Behaviour & Information Technology, 25, 141, 10.1080/01449290500331156
Marczewski, 2015, Even Ninja Monkeys Like to Play, Blurb.
McCallum, S., 2012. Gamification and serious games for personalized health., in: pHealth, pp. 85–96.
Melhart, 2019, Your gameplay says it all: modelling motivation in tom clancy’s the division, 1
Molnar, 2019, Interpretable machine learning, Lulu.com
Morschheuser, 2019, The Gamification of Work: Lessons From Crowdsourcing, Journal of Management Inquiry, 28, 145, 10.1177/1056492618790921
Ng, 2000, Customer retention via data mining, Artificial Intelligence Review, 14, 569, 10.1023/A:1006676015154
Orji, 2017, Improving the efficacy of games for change using personalization models, TOCHI, 24, 32, 10.1145/3119929
Pagulayan, 2002, User-centered design in games, 915
Periáñez, 2016, Churn prediction in mobile social games: Towards a complete assessment using survival ensembles, 564
Rapp, 2019, Strengthening gamification studies: Current trends and future opportunities of gamification research, International Journal of Human-Computer Studies, 127, 1, 10.1016/j.ijhcs.2018.11.007
Reichheld, 2003, Loyalty: A prescription for cutting costs, Marketing Management, 12, 24
Rey, 2011, 1658
Rigby, S., Ryan, R., 2007. The player experience of need satisfaction (pens) model. Immersyve Inc, 1–22.
Rigby, S., Ryan, R.M., 2011. Glued to games: How video games draw us in and hold us spellbound: How video games draw us in and hold us spellbound. AbC-CLIo.
Ryan, 2000, Self-determination theory and the facilitation of intrinsic motivation, social development, and well-being, American Psychologist, 55, 68, 10.1037/0003-066X.55.1.68
Ryan, 2006, The motivational pull of video games: A self-determination theory approach, Motivation and Emotion, 30, 344, 10.1007/s11031-006-9051-8
Sangin, 2018
Scheiner, 2015, The motivational fabric of gamified idea competitions: The evaluation of game mechanics from a longitudinal perspective, Creativity and Innovation Management, 24, 341, 10.1111/caim.12115
Scornet, 2017, Tuning parameters in random forests, ESAIM: Proceedings and Surveys, 60, 144, 10.1051/proc/201760144
Siroky, 2009, Navigating random forests and related advances in algorithmic modeling, Statistics Surveys, 3, 147, 10.1214/07-SS033
Su, 2015, A mobile gamification learning system for improving the learning motivation and achievements, Journal of Computer Assisted Learning, 31, 268, 10.1111/jcal.12088
Tamassia, 2016, Predicting player churn in destiny: A hidden markov models approach to predicting player departure in a major online game, 1
Teng, 2014, Team participation and online gamer loyalty, Electronic Commerce Research and Applications, 13, 24, 10.1016/j.elerap.2013.08.001
Torgo, 2007, Utility-based regression, European Conference on Principles of Data Mining and Knowledge Discovery, Springer, 597
Torgo, 2015, Resampling strategies for regression, Expert Systems, 32, 465, 10.1111/exsy.12081
Warmelink, 2018, 1108
Xi, 2019, Does gamification satisfy needs? a study on the relationship between gamification features and intrinsic need satisfaction, International Journal of Information Management, 46, 210, 10.1016/j.ijinfomgt.2018.12.002
Xie, 2015, Predicting player disengagement and first purchase with event-frequency based data representation, 230
Yannakakis, 2011, Experience-driven procedural content generation, IEEE Transactions on Affective Computing, 2, 147, 10.1109/T-AFFC.2011.6
Yannakakis, 2018, vol. 2
Yee, 2016, The gamer motivation profile: What we learned from 250,000 gamers, 10.1145/2967934.2967937
Zou, 2016, Finding the best classification threshold in imbalanced classification, Big Data Research, 5, 2, 10.1016/j.bdr.2015.12.001