  This paper presents a new approach to select events of interest to a user in
a social media setting where events are generated by the activities of the
user's friends through their mobile devices. We argue that given the unique
requirements of the social media setting, the problem is best viewed as an
inductive learning problem, where the goal is to first generalize from the
users' expressed "likes" and "dislikes" of specific events, then to produce a
program that can be manipulated by the system and distributed to the collection
devices to collect only data of interest. The key contribution of this paper is
a new algorithm that combines existing machine learning techniques with new
program synthesis technology to learn users' preferences. We show that when
compared with the more standard approaches, our new algorithm provides up to
order-of-magnitude reductions in model training time, and significantly higher
prediction accuracies for our target application. The approach also improves on
standard machine learning techniques in that it produces clear programs that
can be manipulated to optimize data collection and filtering.
