Proceedings of the 3rd International Conference on Mechatronics Engineering and Information Technology (ICMEIT 2019)

Comparing Rank Aggregation Methods based on Mallows Model

Authors
Zhangqian Zhu, Xiaomeng Wang, Shigang Qiu
Corresponding Author
Xiaomeng Wang
Available Online April 2019.
DOI
https://doi.org/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.
Open Access
This is an open access article distributed under the CC BY-NC license.

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Proceedings
Part of series
Advances in Computer Science Research
Publication Date
April 2019
ISBN
978-94-6252-708-9
ISSN
2352-538X
DOI
https://doi.org/10.2991/icmeit-19.2019.98How to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

Cite this article

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
PB  - Atlantis Press
SP  - 609
EP  - 616
SN  - 2352-538X
UR  - https://doi.org/10.2991/icmeit-19.2019.98
DO  - https://doi.org/10.2991/icmeit-19.2019.98
ID  - Zhu2019/04
ER  -