  Over the last few years, MaaS has been extensively studied and evolved into
offering a multitude of mobility services that continuously increase, from
alternative car or bike-sharing modes to autonomous vehicles, that aspire to
become a part of this novel ecosystem. MaaS provides end-users with multimodal,
integrated, and digital mobility solutions, including a multitude of different
choices able to cover users specific needs in a personalized manner. This
practically leads to a range of novel MaaS products, that may have complex
structures and the challenge of matching them to user preferences and needs so
that suitable products can be provided to end-users. Moreover, in the everyday
use of MaaS, travelers require support to identify routes to reach their
destination that adhere to their personal preferences and are aligned to the
MaaS product they have purchased. This paper tackles these two user-centric
challenges by exploiting state-of-the-art techniques from the field of
Personalization and Recommendation systems and integrating them in MaaS
platforms and route planning applications.
