Re-ranking Based Diversification: A Unifying View
Information Retrieval
2024-03-29 v1 Machine Learning
Abstract
We analyze different re-ranking algorithms for diversification and show that majority of them are based on maximizing submodular/modular functions from the class of parameterized concave/linear over modular functions. We study the optimality of such algorithms in terms of the `total curvature'. We also show that by adjusting the hyperparameter of the concave/linear composition to trade-off relevance and diversity, if any, one is in fact tuning the `total curvature' of the function for relevance-diversity trade-off.
Cite
@article{arxiv.1906.11285,
title = {Re-ranking Based Diversification: A Unifying View},
author = {Shameem A Puthiya Parambath},
journal= {arXiv preprint arXiv:1906.11285},
year = {2024}
}