论文标题
从不完整的偏好中学习随机实用程序模型的混合物
Learning Mixtures of Random Utility Models with Features from Incomplete Preferences
论文作者
论文摘要
随机实用模型（RUMS）是一种特殊情况，它们是一种特殊情况，是最受欢迎的偏好学习模型之一。在本文中，我们考虑了具有特征及其混合物的朗姆酒，每个替代方案都有一个特征向量，可能在不同的代理之间有所不同。这样的模型显着概括了标准的PL和朗姆酒，但在文献中没有得到很好的研究。我们将朗姆酒与功能的混合物扩展到产生不完整偏好并表征其可识别性的模型。对于PL，我们证明，当具有特征的PL是可识别的时，它的MLE与在轻度假设下的严格凹面目标函数一致，通过表征绑定在根平方 - 元素上（RMSE）的绑定，这自然会导致样品复杂性结合。我们还表征了具有功能的更通用朗姆酒的可识别性，并提出了一般的RBCML来学习它们。我们对合成数据的实验证明了MLE对PL的有效性，其特征具有统计效率和计算效率之间的权衡。我们对现实世界数据的实验显示了PL具有特征及其混合物的预测能力。
Random Utility Models (RUMs), which subsume Plackett-Luce model (PL) as a special case, are among the most popular models for preference learning. In this paper, we consider RUMs with features and their mixtures, where each alternative has a vector of features, possibly different across agents. Such models significantly generalize the standard PL and RUMs, but are not as well investigated in the literature. We extend mixtures of RUMs with features to models that generate incomplete preferences and characterize their identifiability. For PL, we prove that when PL with features is identifiable, its MLE is consistent with a strictly concave objective function under mild assumptions, by characterizing a bound on root-mean-square-error (RMSE), which naturally leads to a sample complexity bound. We also characterize identifiability of more general RUMs with features and propose a generalized RBCML to learn them. Our experiments on synthetic data demonstrate the effectiveness of MLE on PL with features with tradeoffs between statistical efficiency and computational efficiency. Our experiments on real-world data show the prediction power of PL with features and its mixtures.
