English

Max-Sum Diversification, Monotone Submodular Functions and Semi-metric Spaces

Machine Learning 2015-11-10 v1

Abstract

In many applications such as web-based search, document summarization, facility location and other applications, the results are preferable to be both representative and diversified subsets of documents. The goal of this study is to select a good "quality", bounded-size subset of a given set of items, while maintaining their diversity relative to a semi-metric distance function. This problem was first studied by Borodin et al\cite{borodin}, but a crucial property used throughout their proof is the triangle inequality. In this modified proof, we want to relax the triangle inequality and relate the approximation ratio of max-sum diversification problem to the parameter of the relaxed triangle inequality in the normal form of the problem (i.e., a uniform matroid) and also in an arbitrary matroid.

Keywords

Cite

@article{arxiv.1511.02402,
  title  = {Max-Sum Diversification, Monotone Submodular Functions and Semi-metric Spaces},
  author = {Sepehr Abbasi Zadeh and Mehrdad Ghadiri},
  journal= {arXiv preprint arXiv:1511.02402},
  year   = {2015}
}

Comments

This article draws heavily from arXiv:1203.6397 by other authors

R2 v1 2026-06-22T11:39:47.488Z