Discrete Choice and Applications

Modern machine learning and AI have revolutionized the generation of ranking and recommendations across many domains, taking data-driven approaches to inferring the candidate items a user is most likely to select. The theory of discrete choice provides the theoretical underpinnings for study of these problems. This theory is central to economics and behavioral sciences, and was recognized with the 2000 Nobel Prize in economics awarded to Daniel McFadden for his work on the analysis of discrete choice. Classical work in this area, and a wide range of recent advances, have much to offer in thinking about how to support users in making choices. However, many of the central tools of discrete choice are not broadly known to the researchers in our area. In this proposed tutorial, we will cover the foundations of the field, and provide connections to common tools of machine learning such as logistic regression, multinomial regression, and softmax. We will cover a number of results about the representational power and complexity of learning various models of choice. And finally, we will suggest both open problems in the field, and areas where discrete choice tools are relevant to key problems in the Web Conference community.