Tiering as a Stochastic Submodular Optimization Problem
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.
Cite
@article{arxiv.2005.07893,
title = {Tiering as a Stochastic Submodular Optimization Problem},
author = {Hyokun Yun and Michael Froh and Roshan Makhijani and Brian Luc and Alex Smola and Trishul Chilimbi},
journal= {arXiv preprint arXiv:2005.07893},
year = {2020}
}