Journal of the Royal Statistical Society. ![]() Bayesian nonparametric Plackett-Luce models for the analysis of preferences for college degree programmes. Learning to rank: from pairwise approach to listwise approach. Rank analysis of incomplete block designs: I. Plackett-Luce regression: A new Bayesian model for polychotomous data. ![]() Comprehensive experiments on publicly-available real-life datasets showcase the effectiveness of PLRM, as opposed to a pipelined approach of clustering followed by learning to rank, as well as approaches that assume a single ranking function for a heterogeneous population. To address this problem in a joint manner, we develop a probabilistic graphical model called Plackett-Luce Regression Mixture or PLRM model, and describe its inference via Expectation-Maximization algorithm. Because these sub-populations are not known in advance, and are effectively latent, the problem turns into simultaneously learning both a set of ranking functions, as well as the latent assignment of instances to functions. In this work, we are concerned with learning to rank for a heterogeneous population, which may consist of a number of sub-populations, each of which may rank objects differently. In the vast majority of the learning to rank literature, there is an implicit assumption that the population of ranking instances are homogeneous, and thus can be modeled by a single central ranking function. ![]() The objective is to learn a function that ranks a number of instances based on their features. Learning to rank is an important problem in many scenarios, such as information retrieval, natural language processing, recommender systems, etc.
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