论文标题
在《情人》的眼中：通过因果用户建模的稳健预测
In the Eye of the Beholder: Robust Prediction with Causal User Modeling
论文作者
论文摘要
准确地预测项目与用户的相关性对于许多社交平台的成功至关重要。传统方法在记录的历史数据上训练模型；但是，推荐系统，媒体服务和在线市场都表现出不断的新内容涌入 - 使相关性成为一个动人的目标，标准的预测模型并不强大。在本文中，我们为相关预测提出了一个学习框架，该框架对数据分布的变化是可靠的。我们的主要观察结果是，可以通过考虑用户如何感知环境来获得鲁棒性。我们将用户建模为有限理性的决策者，其因果关系由因果图编码，并显示有关该图的最小信息可用于与分布更改抗衡。在多种环境中进行的实验证明了我们方法的有效性。
Accurately predicting the relevance of items to users is crucial to the success of many social platforms. Conventional approaches train models on logged historical data; but recommendation systems, media services, and online marketplaces all exhibit a constant influx of new content -- making relevancy a moving target, to which standard predictive models are not robust. In this paper, we propose a learning framework for relevance prediction that is robust to changes in the data distribution. Our key observation is that robustness can be obtained by accounting for how users causally perceive the environment. We model users as boundedly-rational decision makers whose causal beliefs are encoded by a causal graph, and show how minimal information regarding the graph can be used to contend with distributional changes. Experiments in multiple settings demonstrate the effectiveness of our approach.
