Amazon.com Recommendations: Item-to-Item Collaborative Filtering
IEEE Internet Computing
A grounded investigation of game immersion
CHI '04 Extended Abstracts on Human Factors in Computing Systems
GameFlow: a model for evaluating player enjoyment in games
Computers in Entertainment (CIE) - Theoretical and Practical Computer Applications in Entertainment
An intelligent fuzzy-based recommendation system for consumer electronic products
Expert Systems with Applications: An International Journal
Flow in games (and everything else)
Communications of the ACM
A cognitive approach for agent-based personalized recommendation
Knowledge-Based Systems
Journal of Management Information Systems
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Toward an understanding of flow in video games
Computers in Entertainment (CIE) - Theoretical and Practical Computer Applications in Entertainment
Psychologically structured approach to user experience in games
Proceedings of the 5th Nordic conference on Human-computer interaction: building bridges
The YouTube video recommendation system
Proceedings of the fourth ACM conference on Recommender systems
Multi-attribute auction model for agent-based content trading in telecom markets
KES'10 Proceedings of the 14th international conference on Knowledge-based and intelligent information and engineering systems: Part I
A multi-agent system for game trading on the B2B electronic market
KES-AMSTA'11 Proceedings of the 5th KES international conference on Agent and multi-agent systems: technologies and applications
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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.