Topic
Ordinal regression
About: Ordinal regression is a research topic. Over the lifetime, 1879 publications have been published within this topic receiving 65431 citations.
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TL;DR: This paper introduced an ordinal Coefficient of Consistence and a nonparametric test of paired comparison response circular triads (inconsistencies) to test for ordinal response inconsistencies.
28 citations
TL;DR: Under the proposed framework of kernelized OPP (KOPP), the nonlinear relationship and, more importantly, efficiently fuse acoustic and symbolic features obtained from the artist recommended meta-data are derived.
Abstract: This paper proposes a content-based artist recommendation framework which learns relationships between users' preference and music contents through ordinal regression. In particular, an artist is characterized by the parameters of its corresponding acoustical model which is adapted from a universal background model. These artist-specific acoustic features together with their preference rankings are then used as input vectors for the proposed order preserving projection (OPP) algorithm which tries to find a suitable subspace such that the desired ranking order of the data after projection can be kept as much as possible. The proposed linear OPP can be kernelized to learn the nonlinear relationship between music contents and users' artist rank orders. Under the proposed framework of kernelized OPP (KOPP), we can derive the nonlinear relationship and, more importantly, efficiently fuse acoustic and symbolic features obtained from the artist recommended meta-data. Experimental results demonstrate that OPP attains comparable results with those obtained with a conventional ordinal regression method, Prank. Moreover, by exploring the nonlinear relationship among training examples and combining acoustic and symbolic features, KOPP outperforms previous approaches to artist recommendation.
28 citations
TL;DR: Robust Ordinal Regression is extended to allow a user to declare indifference in case the values of the two solutions do not differ by more than some personal threshold, and several heuristics to pick pairs of solutions to be shown to the decision maker in order to minimize the number of interactions necessary.
28 citations
TL;DR: In this paper, a random effects ordinal quantile regression model is proposed for analysis of longitudinal data with ordinal outcome of interest, and an efficient Gibbs sampling algorithm is derived for fitting the model to the data based on a location-scale mixture representation of the skewed doubleexponential distribution.
Abstract: Since the pioneering work by Koenker and Bassett [27], quantile regression models and its applications have become increasingly popular and important for research in many areas. In this paper, a random effects ordinal quantile regression model is proposed for analysis of longitudinal data with ordinal outcome of interest. An efficient Gibbs sampling algorithm was derived for fitting the model to the data based on a location-scale mixture representation of the skewed double-exponential distribution. The proposed approach is illustrated using simulated data and a real data example. This is the first work to discuss quantile regression for analysis of longitudinal data with ordinal outcome.
28 citations
TL;DR: A mixed integer linear formulation is developed to establish a linear model for the computation of decision recommendations and makes it possible to complete incomplete ordinal information with other forms of incomplete information.
28 citations