Unsupervised Rank Aggregation using Hierarchical User Similarity Clustering

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.