Using program synthesis for social recommendations
Alvin Cheung, Armando Solar-Lezama, Samuel R. Madden · 2012
This paper presents a new approach to select events of interest to users in a social media setting where events are generated from mobile devices. We argue that the problem is best solved by inductive learning, 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 used to collect only data of interest.