Abstract
In order to achieve flow (i.e. complete focus on playing followed by a high level of enjoyment) and increase player retention (i.e. keep a user playing a game longer and more often) it is important that difficulty of the game that a user is playing matches her/his skills. Due to a large amount of different games which are available to users, it is not easy for them to find games which best suit their skills and abilities. In this paper we propose a recommendation algorithm based on the information gathered from users’ interaction with a game. We use that information to model users’ success and progress in the game as well as motivation for playing. Besides, the proposed algorithm also takes into account user preferences, mobile phone characteristics and game related information which is gathered from users once the game is available on the market. Before enough information is gathered from users, the algorithm uses the information gathered during the game development phase and acquired from game developers and testers. In the implemented multi-agent system, after a user finishes playing a game, she/he receives a notification with a list of games which best suit her/his skills and preferences.
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Skocir, P., Marusic, L., Marusic, M., Petric, A. (2012). The MARS – A Multi-Agent Recommendation System for Games on Mobile Phones. In: Jezic, G., Kusek, M., Nguyen, NT., Howlett, R.J., Jain, L.C. (eds) Agent and Multi-Agent Systems. Technologies and Applications. KES-AMSTA 2012. Lecture Notes in Computer Science(), vol 7327. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-30947-2_14
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DOI: https://doi.org/10.1007/978-3-642-30947-2_14
Publisher Name: Springer, Berlin, Heidelberg
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