Nowadays we are living in an era that is overloaded with information.
Decision-making in this environment can sometimes become a nightmare.
There are too many choices and we simply cannot explore them all.
Therefore, it would be really helpful to have a system to help us to
find the right choice. Such systems, which learn user preferences and
provide personalized recommendations to them are called Recommender
Systems. Evidently, the performance of recommender systems depends on
the amount of information that users provide regarding items, most often
in the form of ratings. This problem is amplified for new users because
they have not provided any rating, which impacts negatively on the
quality of generated recommendations. This problem is called new user
problem or cold-start problem. A simple and effective way to overcome
this problem, is by posing queries to new users so that they express
their preferences about selected items, e.g. by rating them.
Nevertheless, the selection of items must take into consideration that
users are not willing to answer a lot of such queries. To address this
problem, active learning methods have been proposed to acquire the most
informative ratings, i.e ratings from users that will help most in
determining their interests. The aim of this thesis is to take
inspiration from the literature of active learning for machine learning
and develop new methods for the new user problem in recommender systems.
In the recommender system context, new users play the role of the Oracle
and provide labels (ratings) to the queries (items). In this approach,
we will take into consideration that although there are no data for new
users, but there is abundant data for existing users. Such additional
data can help us to develop scalable and accurate active learning
methods for the new user problem in recommender systems. The thesis
consists of two parts. In the first part, to be consistent with the
settings of active learning in machine learning and the re
