Choice problems refer to selecting the best choices from several items, and learning users' preferences in choice problems is of great significance in understanding the decision making mechanisms and providing personalized services. Existing works typically assume that people evaluate items independently. In practice, however, users' preferences depend on the market in which items are placed, which is known as context effects; and the order of users' preferences for two items may even be reversed, which is referred to preference reversals. In this work, we identify three factors contributing to context effects: users' adaptive weights, the inter-item comparison, and display positions. We propose a context-dependent preference model named Pacos as a unified framework for addressing three factors simultaneously, and consider two design methods including an additive method with high interpretability and an ANN-based method with high accuracy. We study the conditions for preference reversals to occur and provide an theoretical proof of the effectiveness of Pacos in addressing preference reversals. Experimental results show that the proposed method has better performance than prior works in predicting users' choices, and has great interpretability to help understand the cause of preference reversals.

翻译：选择问题涉及从多个选项中筛选最优选择，而学习用户在其中的偏好对于理解决策机制和提供个性化服务具有重要意义。现有研究通常假设人们对选项进行独立评估。然而实际场景中，用户的偏好会随选项所处的市场环境变化，这一现象称为情境效应；甚至用户对两个选项的偏好顺序可能发生反转，即偏好反转。本文识别出导致情境效应的三个因素：用户自适应权重、选项间比较及展示位置。我们提出了一种名为Pacos的情境依赖偏好模型作为统一框架，同时处理这三个因素，并设计了两种实现方法：具有高可解释性的加法方法与基于人工神经网络的高精度方法。我们研究了偏好反转的发生条件，并从理论上证明了Pacos应对偏好反转的有效性。实验结果表明，所提方法在预测用户选择方面优于先前研究，且具备强大的可解释性，有助于理解偏好反转的成因。