Unsupervised Rank Aggregation using Hierarchical User Similarity Clustering

被引:0
|
作者
Dutta, Sourav [1 ]
机构
[1] Max Planck Inst Informat, Saarbrucken, Germany
关键词
Rank Aggregation; Kendall-tau distance; Kemeny optimality; Clustering;
D O I
10.3233/978-1-61499-589-0-37
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Given multiple user-input rank lists, rank aggregation or combining the rankings to obtain a consensus (joint ordering) provides an interesting and classical domain of research, pertinent to applications across information retrieval, natural language processing, web search, etc. Efficient computation of such joint ranking poses a challenging task as optimal rank aggregation based on the Kemeny measure has been shown to be NP-hard. This paper proposes a novel rank aggregation framework, CRAAR, incorporating a linear combination of the input rank lists, based on user groups, exhibiting similar ranking preferences, obtained via unsupervised hierarchical clustering. To this end, we also present the Accordance Ratio as a measure to capture the inter-user preference similarity. Extensive experiments on real datasets show an improved performance of our approach (based on optimal Kemeny ranking) over state-of-the-art, thereby better capturing the preference of the majority.
引用
收藏
页码:37 / 47
页数:11
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