Comparing Rank Aggregation Methods based on Mallows Model
Zhangqian Zhu, Xiaomeng Wang, Shigang Qiu
Available Online April 2019.
- https://doi.org/10.2991/icmeit-19.2019.98How to use a DOI?
- Rank aggregation; Mallows model; rank-biased overlap.
- 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.
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 -