Typically, recommender systems from any domain, be it movies, music,
restaurants, etc., are organized in a centralized fashion. The service provider
holds all the data, biases in the recommender algorithms are not transparent to
the user, and the service providers often create lock-in effects making it
inconvenient for the user to switch providers. In this paper, we argue that the
user's smartphone already holds a lot of the data that feeds into typical
recommender systems for movies, music, or POIs. With the ubiquity of the
smartphone and other users in proximity in public places or public
transportation, data can be exchanged directly between users in a
device-to-device manner. This way, each smartphone can build its own database
and calculate its own recommendations. One of the benefits of such a system is
that it is not restricted to recommendations for just one user - ad-hoc group
recommendations are also possible. While the infrastructure for such a platform
already exists - the smartphones already in the palms of the users - there are
challenges both with respect to the mobile recommender system platform as well
as to its recommender algorithms. In this paper, we present a mobile
architecture for the described system - consisting of data collection, data
exchange, and recommender system - and highlight its challenges and
opportunities.Comment: Accepted for publication at the 2019 IEEE 16th International
Conference on Ubiquitous Intelligence and Computing (IEEE UIC 2019