  Pervasive computing systems employ distributed and embedded devices in order
to raise, communicate, and process data in an anytime-anywhere fashion.
Certainly, its most prominent device is the smartphone due to its wide
proliferation, growing computation power, and wireless networking capabilities.
In this context, we revisit the implementation of digitalized word-of-mouth
that suggests exchanging item preferences between smartphones offline and
directly in immediate proximity. Collaboratively and decentrally collecting
data in this way has two benefits. First, it allows to attach for instance
location-sensitive context information in order to enrich collected item
preferences. %enhance on-device recommendations. Second, model building does
not require network connectivity. Despite the benefits, the approach naturally
raises data privacy and data scarcity issues. In order to address both, we
propose Propagate and Filter, a method that translates the traditional approach
of finding similar peers and exchanging item preferences among each other from
the field of decentralized to that of pervasive recommender systems.
Additionally, we present preliminary results on a prototype mobile application
that implements the proposed device-to-device information exchange. Average
ad-hoc connection delays of 25.9 seconds and reliable connection success rates
within 6 meters underpin the approach's technical feasibility.
