Who is the winner? Machine Learning in the preference aggregation methods.

Speaker: Hanna Kujawska

Title: Who is the winner? Machine Learning in the preference aggregation methods.

Abstract:   We are living in a data-driven world. The booming IT industry collects large amounts of user preferences and behavioral data to make versatile decisions – for example, how voters rank candidates, consumers choose one product over another, search engines rank webpages. Preference aggregation is the process of combining multiple individually ranked lists of alternatives towards choosing a winner from the list of options, but the winner can be difficult to compute. Ratings and rankings, and thus preference aggregation methods are widely used, but how exactly do they work? Who is the winner?

This seminar presentation aims to share and discuss recent progress in machine learning and decision-making from rank data, as well as provide an overview of the fundamental ideas behind mathematical rank systems, i.e. reference to classical properties and algorithms for handling ordinal data. The question we explore is can winners or representative rankings be predicted using machine learning methods. Specifically, we focus on the Borda count and Kemeny method and explore XGBoost, Linear Support Vector Machines, Multilayer Perceptron and regularized linear classifiers with stochastic gradient descent. We analyze the capabilities of those approaches on two datasets: one obtained from Spotify and a high-dimension synthetic dataset, with the goal of establishing the best trade-off between search time and performance.

For registration, please send an email (before 10th of June) to Ahmad Hemmati.