论文标题
学习选择功能通过帕累托式插头
Learning Choice Functions via Pareto-Embeddings
论文作者
论文摘要
我们考虑学习从给定的对象组中进行选择的问题，其中每个对象都由特征向量表示。选择建模中的传统方法主要基于学习潜在的，实用的实用程序功能，从而在选择替代方案上引起线性顺序。尽管此方法适合离散（TOP-1）选择，但如何将其用于子集选择并不是一件直接的。我们建议将它们嵌入到更高维度的公用事业空间中，而不是将选择替代品映射选择替代品，在该空间中，我们可以在其中识别出具有帕累托最佳点的选择集。为此，我们提出了一种学习算法，该算法将适合本任务的可区分损失函数最小化。我们证明了在一组基准数据集上学习帕累托的可行性。
We consider the problem of learning to choose from a given set of objects, where each object is represented by a feature vector. Traditional approaches in choice modelling are mainly based on learning a latent, real-valued utility function, thereby inducing a linear order on choice alternatives. While this approach is suitable for discrete (top-1) choices, it is not straightforward how to use it for subset choices. Instead of mapping choice alternatives to the real number line, we propose to embed them into a higher-dimensional utility space, in which we identify choice sets with Pareto-optimal points. To this end, we propose a learning algorithm that minimizes a differentiable loss function suitable for this task. We demonstrate the feasibility of learning a Pareto-embedding on a suite of benchmark datasets.
