Choice functions accept a set of alternatives as input and produce a preferred subset of these alternatives as output. We study the problem of learning such functions under conditions of context-dependence of preferences, which means that the preference in favor of a certain choice alternative may depend on what other options are also available. In spite of its practical relevance, this kind of context-dependence has received little attention in preference learning so far. We propose a suitable model based on context-dependent (latent) utility functions, thereby reducing the problem to the task of learning such utility functions. Practically, this comes with a number of challenges. For example, the set of alternatives provided as input to a choice function can be of any size, and the output of the function should not depend on the order in which the alternatives are presented. To meet these requirements, we propose two general approaches based on two representations of context-dependent utility functions, as well as instantiations in the form of appropriate end-to-end trainable neural network architectures. Moreover, to demonstrate the performance of both networks, we present extensive empirical evaluations on both synthetic and real-world datasets.
翻译:我们研究了在偏好的背景依赖条件下学习这种功能的问题,这意味着偏好某一选择的备选办法可能取决于其他可选办法的先后顺序。尽管这种背景依赖性在实际相关性上没有多少受到重视,但迄今为止在偏爱学习方面却很少受到重视。我们提出了一个基于基于背景(相对)实用功能的适当模式,从而将问题降低到学习这种实用功能的任务。实际上,这带来了一些挑战。例如,作为选择功能投入而提供的一套替代方法可以是任何大小的,而该功能的产出不应取决于提出替代品的先后顺序。为了满足这些要求,我们根据两种基于背景的实用功能的表述提出了两种一般性办法,以及以适当的端到端的、可训练的神经网络结构为形式的瞬间反应。此外,为了展示这两个网络的绩效,我们提出了关于合成和真实世界数据集的广泛经验评估。