Corrected Evolutive Kendall's tau Coefficients for Incomplete Rankings with Ties: Application to Case of Spotify Lists

Abstract

[EN] Mathematical analysis of rankings is essential for a wide range of scientific, public, and industrial applications (e.g., group decision-making, organizational methods, R&D sponsorship, recommender systems, voter systems, sports competitions, grant proposals rankings, web searchers, Internet streaming-on-demand media providers, etc.). Recently, some methods for incomplete aggregate rankings (rankings in which not all the elements are ranked) with ties, based on the classic Kendall's tau coefficient, have been presented. We are interested in ordinal rankings (that is, we can order the elements to be the first, the second, etc.) allowing ties between the elements (e.g., two elements may be in the first position). We extend a previous coefficient for comparing a series of complete rankings with ties to two new coefficients for comparing a series of incomplete rankings with ties. We make use of the newest definitions of Kendall's tau extensions. We also offer a theoretical result to interpret these coefficients in terms of the type of interactions that the elements of two consecutive rankings may show (e.g., they preserve their positions, cross their positions, and they are tied in one ranking but untied in the other ranking, etc.). We give some small examples to illustrate all the newly presented parameters and coefficients. We also apply our coefficients to compare some series of Spotify charts, both Top 200 and Viral 50, showing the applicability and utility of the proposed measures.This research was funded by the Spanish Government, Ministerio de Economia y Competividad, grant number MTM2016-75963-P.Pedroche Sánchez, F.; Conejero, JA. (2020). Corrected Evolutive Kendall's tau Coefficients for Incomplete Rankings with Ties: Application to Case of Spotify Lists. Mathematics. 8(10):1-30. https://doi.org/10.3390/math8101828S130810Diaconis, P., & Graham, R. L. (1977). Spearman’s Footrule as a Measure of Disarray. 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