Description
Visitors of Distributed Events, such as the Long Night of Museums in Munich, are facing various challenges due to the huge amount of available events. This thesis presents concepts and solutions for developing an assistance system that supports these visitors. The system combines various methods and technologies from the field of artificial intelligence, especially recommender systems and information retrieval techniques, as well as planning algorithms for tours with time-constrained events.
Content-based and collaborative recommender systems are connected with a novel knowledge-based recommender in order to suggest events that can be combined to a time-optimized tour but also match the user's interests. Additionally, the user is supported via a search engine that is customized for this application domain. Tour planning algorithms are used to determine an appropriate order of the selected events. In case of unexpected events the generated tours can be adapted in a smart manner on the go.
Log data acquired in various field studies were evaluated via descriptive and inductive statistical methods. The results prove the utility and acceptance of the provided assistance.
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