Pacos: Modeling preference reversals in users’ context-dependent choices

Abstract

Choice problems refer to the problem of selecting the best choices from several available items, and learning users’ preferences in choice problems is of great importance in understanding users’ 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 the context effects; and the order of users’ preferences for two items may even be reversed, which is called preference reversals. In this work, we identify three factors contributing to the 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 to address 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 a theoretical proof of the effectiveness of Pacos in predicting when preference reversals would occur. 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.