EEML 2012 – Experimental Economics in Machine Learning
In Experimental Economics, laboratory and feld experiments are conducted on subjects in order to improve theoretical knowledge about human behavior in interactions. Although paying different amounts of money restricts the preferences of the subjects in experiments, the exclusive application of analytical game theory does not suce to explain the recorded data. It exacts the development and evaluation of more sophisticated models. In some experiments, human subjects are involved into an interaction with automated agents and these agents are used for simulating human interactions. The more data is used for the evaluation, the more of statistical signicance can be achieved. Since huge amounts of behavioral data are required to be scanned for regularities and automated agents are required to simulate and to intervene human interactions, Machine Learning is the tool of choice for the research in Experimental Economics. Moreover modern economics extensively involves network structures, which can be modeled as graphs or more complicated relational structures.
We describe a new recommender system for the Russian interactive radio network FMhost. The new recommender model combines collaborative and user-based approaches. The system extracts information from tags of listened tracks for matching user and radio station profiles and follows an adaptive online learning strategy based on user history. We also provide some basic examples and describe the quality of service evaluation methodology.
В статье описана новая рекомендательная система для российской интерактивной радиосети FMhost. Система извлекает информацию из тегов прослушанных треков для соответствующего пользователя и профилей радиостанции и следует адаптивной стратегии онлайн обучения на основе пользовательских истории. Приведены примеры и оценка качества методологии.