Towards the Identification of Players’ Profiles Using Game’s Data Analysis Based on Regression Model and Clustering [r-libre/908]

Benmakrelouf, Souhila; Mezghani, Neila, & Kara, Nadjia (2015). Towards the Identification of Players’ Profiles Using Game’s Data Analysis Based on Regression Model and Clustering. In Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (p. 1403-1410). ACM. ISBN 978-1-4503-3854-7 https://doi.org/10.1145/2808797.2809429

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Item Type:	Papers in Conference Proceedings
Refereed:	Yes
Status:	Published
Abstract:	Personalization of serious games is an important factor for motivating and engaging players. It requires the identification of players’ profiles through the analysis of large volume of data including game data. This research study aims at identifying relevant data from an online serious game and the appropriate data mining methods for deduction of players’ profiles. Multiple linear regression is applied to analyze the influence of player’s characteristics on his performance. Moreover, clustering technique is used, in particular K-means, to extract players’ clusters and to identify their common characteristics. The regression models showed that the number of access to the game, completed quests and advantages used contribute significantly to the scores and the gaming duration, while the clustering revealed three forms of players’ participation: beginner, intermediate and advanced; who interact with the game according to their experiences.
Official URL:	http://dx.doi.org/10.1145/2808797.2809429
Depositor:	Mezghani, Neila
Owner / Manager:	Neila Mezghani
Deposited:	30 Mar 2016 13:52
Last Modified:	28 Oct 2019 17:48

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