Using Program Synthesis for Social Recommendations
This addresses the challenge of efficient and accurate event recommendation in social media for users and system optimization, though it is incremental as it combines existing techniques with new program synthesis.
The paper tackles the problem of selecting relevant events for users in social media by learning user preferences from likes and dislikes, resulting in order-of-magnitude reductions in training time and significantly higher prediction accuracies.
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.