Tiering as a Stochastic Submodular Optimization Problem

• URL: http://arxiv.org/abs/2005.07893v1
• Date: Sat, 16 May 2020 07:39:29 GMT
• Title: Tiering as a Stochastic Submodular Optimization Problem
• Authors: Hyokun Yun, Michael Froh, Roshan Makhijani, Brian Luc, Alex Smola, Trishul Chilimbi
• Abstract summary: Tiering is an essential technique for building large-scale information retrieval systems. We show that the optimal tiering as an optimization problem can be cast as a submodular minimization problem with a submodular knapsack constraint.
• Score: 5.659969270836789
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Tiering is an essential technique for building large-scale information retrieval systems. While the selection of documents for high priority tiers critically impacts the efficiency of tiering, past work focuses on optimizing it with respect to a static set of queries in the history, and generalizes poorly to the future traffic. Instead, we formulate the optimal tiering as a stochastic optimization problem, and follow the methodology of regularized empirical risk minimization to maximize the \emph{generalization performance} of the system. We also show that the optimization problem can be cast as a stochastic submodular optimization problem with a submodular knapsack constraint, and we develop efficient optimization algorithms by leveraging this connection.

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