Comparing Rank Aggregation Methods based on Mallows Model

DOI

10.2991/icmeit-19.2019.98How to use a DOI?

Keywords

Rank aggregation; Mallows model; rank-biased overlap.

Abstract

Rank aggregation is the process of aggregating multiple base rankers into a single but more comprehensive ranker, which plays an important role in many domains such as recommender system, meta-search, database, genomics, etc. Works related to the comparison of rank aggregation methods all don’t have a suitable and general data generation mechanism to produce data with various characteristics and lack a more reasonable and effective algorithm evaluation performance index. Therefore, this paper presents a general data generation mechanism based on Mallows model to produce synthetic controllable datasets, uses generalized Kendall rank correlation coefficient and rank-biased overlap to evaluate and compare the performance of two kinds of methods under different settings. Besides, we also consider the comparison between indices and the impact of data characteristics on the algorithms. This paper may be helpful to researchers and decision-makers from multiple domains.

Copyright

© 2019, 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  - CONF
AU  - Zhangqian Zhu
AU  - Xiaomeng Wang
AU  - Shigang Qiu
PY  - 2019/04
DA  - 2019/04
TI  - Comparing Rank Aggregation Methods based on Mallows Model
BT  - Proceedings of the 3rd International Conference on Mechatronics Engineering and Information Technology (ICMEIT 2019)
PB  - Atlantis Press
SP  - 609
EP  - 616
SN  - 2352-538X
UR  - https://doi.org/10.2991/icmeit-19.2019.98
DO  - 10.2991/icmeit-19.2019.98
ID  - Zhu2019/04
ER  -