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.

通过理解个体的偏好和选择方式，构建能够与其预期十分匹配的产品，并将经济学、决策理论原则融入学习过程中，本文提出了一种以高斯过程为基础的偏好学习模型的完整框架，并介绍了一些用于填补现有文献中空白的新颖基于高斯过程的模型。