Unsupervised Rank Aggregation using Hierarchical User Similarity Clustering
Dutta Sourav · Frontiers in artificial intelligence and applications · 2015
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.