  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.
