Data-driven rank ordering - a preference-based comparison study

DOI

10.2991/ijcis.2011.4.2.3How to use a DOI?

Keywords

rank ordering, preference learning, preference with level of dominance

Abstract

Data driven rank ordering refers to the rank ordering of new data items based on the ordering inherent in existing data items. This is a challenging problem, which has received increasing attention in recent years in the machine learning community. Its applications include product recommendation, information retrieval, financial portfolio construction, and robotics. It is common to construct ordering functions based on binary pairwise preferences. The level of dominance within pairs has been modelled in approaches based on statistical models, where strong assumptions about the distributions of the data are present. For learning pairwise preferences from the data we introduce a distribution-independent framework incorporating the level of dominance. We compare our approach with learning to rank order based on binary pairwise preferences through experiments using large margin classifiers.

Copyright

© 2011, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - JOUR
AU  - Maria Dobrska
AU  - Hui Wang
AU  - William Blackburn
PY  - 2011
DA  - 2011/04/01
TI  - Data-driven rank ordering - a preference-based comparison study
JO  - International Journal of Computational Intelligence Systems
SP  - 142
EP  - 152
VL  - 4
IS  - 2
SN  - 1875-6883
UR  - https://doi.org/10.2991/ijcis.2011.4.2.3
DO  - 10.2991/ijcis.2011.4.2.3
ID  - Dobrska2011
ER  -