Using program synthesis for social recommendations

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. The key contribution of this paper is a new algorithm that combines machine learning techniques with 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.1

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