Preference modelling lies at the intersection of economics, decision theory, machine learning and statistics. By understanding individuals' preferences and how they make choices, we can build products that closely match their expectations, paving the way for more efficient and personalised applications across a wide range of domains. The objective of this tutorial is to present a cohesive and comprehensive framework for preference learning with Gaussian Processes (GPs), demonstrating how to seamlessly incorporate rationality principles (from economics and decision theory) into the learning process. By suitably tailoring the likelihood function, this framework enables the construction of preference learning models that encompass random utility models, limits of discernment, and scenarios with multiple conflicting utilities for both object- and label-preference. This tutorial builds upon established research while simultaneously introducing some novel GP-based models to address specific gaps in the existing literature.

翻译：偏好建模处于经济学、决策理论、机器学习与统计学的交叉领域。通过理解个体偏好及其决策机制，我们能够构建更贴合用户期望的产品，从而为广泛领域实现更高效与个性化的应用铺平道路。本教程旨在提出一个连贯而全面的高斯过程（GPs）偏好学习框架，展示如何将（源自经济学与决策理论的）理性原则无缝整合到学习过程中。通过恰当地设计似然函数，该框架能够构建涵盖随机效用模型、辨别阈值以及多目标冲突效用场景的偏好学习模型，同时适用于对象偏好与标签偏好两种情境。本教程在既有研究基础上，引入了若干新型高斯过程模型以弥补现有文献中的特定空白。