Max-Sum diversification, monotone submodular functions and dynamic updates
Allan Borodin, Hyun Chul Lee, Yuli Ye · 2012
Result diversification has many important applications in databases, operations research, information retrieval, and finance. In this paper, we study and extend a particular version of result diversification, known as max-sum diversification. More specifically, we consider the setting where we are given a set of elements in a metric space and a set valuation function f defined on every subset. For any given subset S, the overall objective is a linear combination of f(S) and the sum of the distances induced by S. The goal is to find a subset S satisfying some constraints that maximizes the overall objective.