Authors:
Efstathios Lempesis
and
Christos Makris
Affiliation:
University of Patras, Greece
Keyword(s):
Ranking, Learning-to-Rank, Clustering, Relational Ranking, Web Information Filtering and Retrieval, Searching and Browsing, Text Mining.
Related
Ontology
Subjects/Areas/Topics:
Searching and Browsing
;
Web Information Systems and Technologies
;
Web Interfaces and Applications
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.