Using program synthesis for social recommendations
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Madden_Using program.pdf
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318.74 KB
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cc959dbebd12d94776dd998e76af2dcb
Author(s)
Cheung, Alvin K.
Solar-Lezama, Armando
Madden, Samuel R.
Date Issued
October 2012
Journal
Proceedings of the 21st ACM International Conference on Information and Knowledge Management - CIKM '12
Publisher
Association for Computing Machinery
Citation
Cheung, Alvin, Armando Solar-Lezama, and Samuel Madden. “Using Program Synthesis for Social Recommendations.” Proceedings of the 21st ACM International Conference on Information and Knowledge Management - CIKM’12 (2012). October 29-November 2, 2012, Maui, Hawaii, ACM, New York, NY, USA. p.1732-1736.
Version
Original manuscript
Abstract
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.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1145/2396761.2398507
