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：选择问题是指从几个项目中选择最佳选择,学习用户在选择问题上的偏好对于理解决策机制和提供个性化服务非常重要。现有工作通常假定人们独立评估项目。但在实践中,用户的偏好取决于项目放置的市场,这被称为背景效应;用户对两个项目的偏好顺序甚至可以逆转,这是指偏好逆转。在这项工作中,我们确定了造成背景效应的三个因素:用户的适应权重、项目间比较和显示位置。我们提出了一个基于背景的偏爱模式,名为Pacos,作为同时处理三个因素的统一框架,并考虑两种设计方法,包括解释性强的添加法和以ANNE为基础的方法。我们研究偏好发生的条件,并提供理论证据,证明Pacos在处理偏向逆转方面的有效性。实验结果表明,拟议的方法在预测用户选择方面比先前的工作表现更好,而且具有很大的可解释性,有助于理解偏好的原因。</s>