Machine Learning Coffee Seminar: "Probabilistic Preference Learning With The Mallows Rank Model" Elja Arjas, University of Helsinki

2017-10-16 09:15:00 2017-10-16 10:00:00 Europe/Helsinki Machine Learning Coffee Seminar: "Probabilistic Preference Learning With The Mallows Rank Model" Elja Arjas, University of Helsinki Weekly seminars held jointly by Aalto University and the University of Helsinki. http://cs.aalto.fi/en/midcom-permalink-1e7a8092b94fffaa80911e78363d7371fa8aa95aa95 Gustaf Hällströmin katu 2B, 02150, Helsinki

Weekly seminars held jointly by Aalto University and the University of Helsinki.

16.10.2017 / 09:15 - 10:00
Exactum D123, Gustaf Hällströmin katu 2B, 02150, Helsinki, FI

Helsinki region machine learning researchers will start our week by an exciting machine learning talk. The aim is to gather people from different fields of science with interest in machine learning. Porridge and coffee is served at 9:00 and the talk will begin at 9:15. The venue for this talk is seminar room Exactum D123, Kumpula.

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Probabilistic Preference Learning With The Mallows Rank Model

Elja Arjas
Professor (Emeritus) of Mathematics and Statistics, University of Helsinki

Abstract:

Ranking and comparing items is crucial for collecting information about preferences in many areas, from marketing to politics. The Mallows rank model is among the most successful approaches to analyse rank data, but its computational complexity has limited its use to a form based on Kendall distance. Here, new computationally tractable methods for Bayesian inference in Mallows models are developed that work with any right-invariant distance. The method performs inference on the consensus ranking of the items, also when based on partial rankings, such as top-k items or pairwise comparisons. When assessors are many or heterogeneous, a mixture model is proposed for clustering them in homogeneous subgroups, with cluster-specific consensus rankings. Approximate stochastic algorithms are introduced that allow a fully probabilistic analysis, leading to coherent quantification of uncertainties. The method can be used, for example, for making probabilistic predictions on the class membership of assessors based on their ranking of just some items, and for predicting missing individual preferences, as needed in recommendation systems.

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