Preference relations based unsupervised rank aggregation for metasearch
作者：
Highlights：
• We propose a preference relations based unsupervised rank aggregation algorithm.
• The algorithm gives weights to input rankers depending on their qualities.
• Ranker quality is estimated in unsupervised way using a variant of majority opinion.
• Performed experimental evaluation using supervised and unsupervised metrics.
• Kendall-Tau distance not suitable for evaluating metasearch algorithms.
摘要
•We propose a preference relations based unsupervised rank aggregation algorithm.•The algorithm gives weights to input rankers depending on their qualities.•Ranker quality is estimated in unsupervised way using a variant of majority opinion.•Performed experimental evaluation using supervised and unsupervised metrics.•Kendall-Tau distance not suitable for evaluating metasearch algorithms.
论文关键词：Rank aggregation,Metasearch,Information retrieval
论文评审过程：Received 9 December 2014, Revised 27 August 2015, Accepted 7 December 2015, Available online 18 December 2015, Version of Record 5 January 2016.
论文官网地址：https://doi.org/10.1016/j.eswa.2015.12.005
