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Scalable Hierarchical Multitask Learning Algorithms for Conversion Optimization in Display Advertising

Amr Ahmed
Abhimanyu Das
Alexander J. Smola
ACM International Conference on Web Search And Data Mining (WSDM) (2014)


Many estimation tasks come in groups and hierarchies of related problems. In this paper we propose a hierarchical model and a scalable algorithm to perform inference for multitask learning. It infers task correlation and subtask structure in a joint sparse setting. Implementation is achieved by a distributed subgradient oracle and the successive application of prox-operators pertaining to groups and sub-groups of variables. We apply this algorithm to conversion optimization in display advertising. Experimental results on over 1TB data for up to 1 billion observations and 1 million attributes show that the algorithm provides significantly better prediction accuracy while simultaneously being efficiently scalable by distributed parameter synchronization.