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While user-modeling and recommender systems successfully utilize items likeemails, news, and movies, they widely neglect mind-maps as a source for usermodeling. We consider this a serious shortcoming since we assume user modelingbased on mind maps to be equally effective as user modeling based on otheritems. Hence, millions of mind-mapping users could benefit from user-modelingapplications such as recommender systems. The objective of this doctoral thesisis to develop an effective user-modeling approach based on mind maps. Toachieve this objective, we integrate a recommender system in our mind-mappingand reference-management software Docear. The recommender system builds usermodels based on the mind maps, and recommends research papers based on the usermodels. As part of our research, we identify several variables relating tomind-map-based user modeling, and evaluate the variables' impact onuser-modeling effectiveness with an offline evaluation, a user study, and anonline evaluation based on 430,893 recommendations displayed to 4,700 users. Wefind, among others, that the number of analyzed nodes, modification time,visibility of nodes, relations between nodes, and number of children andsiblings of a node affect the effectiveness of user modeling. When allvariables are combined in a favorable way, this novel approach achievesclick-through rates of 7.20%, which is nearly twice as effective as the bestbaseline. In addition, we show that user modeling based on mind maps performsabout as well as user modeling based on other items, namely the researcharticles users downloaded or cited. Our findings let us to conclude that usermodeling based on mind maps is a promising research field, and that developersof mind-mapping applications should integrate recommender systems into theirapplications. Such systems could create additional value for millions ofmind-mapping users.

Towards Effective Research-Paper Recommender Systems and User Modeling based on Mind Maps