  While user-modeling and recommender systems successfully utilize items like
emails, news, and movies, they widely neglect mind-maps as a source for user
modeling. We consider this a serious shortcoming since we assume user modeling
based on mind maps to be equally effective as user modeling based on other
items. Hence, millions of mind-mapping users could benefit from user-modeling
applications such as recommender systems. The objective of this doctoral thesis
is to develop an effective user-modeling approach based on mind maps. To
achieve this objective, we integrate a recommender system in our mind-mapping
and reference-management software Docear. The recommender system builds user
models based on the mind maps, and recommends research papers based on the user
models. As part of our research, we identify several variables relating to
mind-map-based user modeling, and evaluate the variables' impact on
user-modeling effectiveness with an offline evaluation, a user study, and an
online evaluation based on 430,893 recommendations displayed to 4,700 users. We
find, among others, that the number of analyzed nodes, modification time,
visibility of nodes, relations between nodes, and number of children and
siblings of a node affect the effectiveness of user modeling. When all
variables are combined in a favorable way, this novel approach achieves
click-through rates of 7.20%, which is nearly twice as effective as the best
baseline. In addition, we show that user modeling based on mind maps performs
about as well as user modeling based on other items, namely the research
articles users downloaded or cited. Our findings let us to conclude that user
modeling based on mind maps is a promising research field, and that developers
of mind-mapping applications should integrate recommender systems into their
applications. Such systems could create additional value for millions of
mind-mapping users.
