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、机器学习和统计学的交叉领域。通过理解个体偏好及其选择方式，我们可以构建更贴合用户期望的产品，从而为跨领域的高效个性化应用铺平道路。本教程旨在为基于高斯过程（GPs）的偏好学习提供一个连贯且全面的框架，展示如何将（经济学和决策理论中的）理性原则无缝融入学习过程。通过合理定制似然函数，该框架能够构建涵盖随机效用模型、辨别极限以及存在多重冲突效用的对象偏好与标签偏好场景的偏好学习模型。本教程在已有研究基础上，同时引入若干新型高斯过程模型以填补现有文献中的特定空白。