Personalization is a long-standing problem in data mining and machine learning. Companies make personalized product recommendations to millions of users every second. In addition to the recommendation problem, with the emerging of personal devices, many conventional problems, e.g., recognition, need to be personalized as well. Moreover, as the number of users grows huge, solving personalization becomes quite challenging. In this paper, we formalize the generic personalization problem as an optimization problem. We propose several ADMM algorithms to solve this problem in a distributed way including a new Asynchronous ADMM that removes all synchronous barriers to maximize the training throughput. We provide a mathematical analysis to show that the proposed Asynchronous ADMM algorithm holds a linear convergence rate which is the best to our knowledge. The distributed personalization allows training to be performed in either a cluster or even on a user's device. This can improve the privacy protection as no personal data is uploaded, while personal models can still be shared with each other. We apply this approach to two industry problems, \emph{Facial Expression Recognition} and \emph{Job Recommendation}. Experiments demonstrate more than 30\% relative error reduction on both problems. Asynchronous ADMM allows faster training for problems with millions of users since it eliminates all network I/O waiting time to maximize the cluster CPU throughput. Experiments demonstrate 4 times faster than original synchronous ADMM algorithm.
['Xu Miao', 'Chun-Te Chu', 'Lijun Tang', 'Yitong Zhou', 'Joel D. Young', 'Anmol Bhasin']
Distributed Personalization