Periodic Reporting for period 1 - Gamebooster (Promoting the Efficiency of the European Online Games Industry through an adaptive gaming platform and portal.)
Berichtszeitraum: 2017-06-01 bis 2017-09-30
The Gamebooster platform carries out automatic and user-driven optimisation of the game experience, from mechanical gameplay parameters, to the platform itself prioritising games that garner positive feedback from our user base. This is achieved via machine learning algorithms that identify user behaviour that indicates a quality game, and typical user preferences for gameplay. Our development plan intends to apply Big Data and Deep Learning systems in a more targeted manner than that used by other platforms. Our platform focuses on HTML5 games rather than the older, less-secure, and proprietary Flash package. By employing this protocol, games published to this standard are inherently capable of being played on multiple platforms and even being embedded in instant messaging services such as Facebook Messenger. Where we truly add value is combining analytics with the following traits – limited connection through social networks, cross-platform compatibility, and repeat visits. This allows the tracking of network diffusion of games and products, without the “noise” in a system that is simply attempting to capture as much data in as possible in its dragnet. We break away from the hazardous and unethical revenue models of other social game delivery platforms like Facebook, the Google Play Store, and the AppStore. They rely on indiscriminate harvesting and sale of user data, and the unfettered serving of ads which exposes their end users (and true source of revenue, not the ads they serve) to significant risks. This includes the loss of personal data through breaches, or hostile/unethical actors simply buying access to the “data fountain”, and malware through advertisements. As such we not only improve the gaming experience, we reduce the danger our users face when innocently enjoying their free time.
The general objective of our project is to expand our platform to full-scale functionality with respect to the optimisation and recommendation engine, the Big Data and Deep Learning platform, and monitoring and visualisation of gathered data.