Combining Learning-to-Rank with Clustering

Efstathios Lempesis, Christos Makris

2014

Abstract

This paper aims to combine learning-to-rank methods with an existing clustering underlying the entities to be ranked. In recent years, learning-to-rank has attracted the interest of many researchers and a large number of algorithmic approaches and methods have been published. Existing learning-to-rank methods have as goal to automatically construct a ranking model from training data. Usually, all these methods don't take into consideration the data's structure. Although there is a novel task named “Relational Ranking” which tries to make allowances for the inter-relationship between documents, it has restrictions and it is difficult to be applied in a lot of real applications. To address this problem, we create a per query clustering using state of the art algorithms from our training data. Then, we experimentally verify the effect of clustering on them.

Paper Citation

in Harvard Style

Lempesis E. and Makris C. (2014). Combining Learning-to-Rank with Clustering . In Proceedings of the 10th International Conference on Web Information Systems and Technologies - Volume 2: WEBIST, ISBN 978-989-758-024-6, pages 286-294. DOI: 10.5220/0004846802860294

in Bibtex Style

@conference{webist14,
author={Efstathios Lempesis and Christos Makris},
title={Combining Learning-to-Rank with Clustering},
booktitle={Proceedings of the 10th International Conference on Web Information Systems and Technologies - Volume 2: WEBIST,},
year={2014},
pages={286-294},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004846802860294},
isbn={978-989-758-024-6},
}

in EndNote Style

TY - CONF
JO - Proceedings of the 10th International Conference on Web Information Systems and Technologies - Volume 2: WEBIST,
TI - Combining Learning-to-Rank with Clustering
SN - 978-989-758-024-6
AU - Lempesis E.
AU - Makris C.
PY - 2014
SP - 286
EP - 294
DO - 10.5220/0004846802860294