Choice Model

Choice models aim to predict individual or collective decisions among multiple options, driven by factors like preferences, context, and social influence. Current research emphasizes developing more accurate and efficient models, incorporating diverse data types (e.g., images, network structures) and leveraging advanced architectures like neural networks, graph neural networks, and Gaussian processes to capture complex relationships and heterogeneity. These advancements have significant implications for various fields, improving the accuracy of predictions in areas such as recommendation systems, transportation planning, and marketing, and enabling better resource allocation and policy design.