Learning to Rank from Implicit Feedback

Whenever access to information is mediated by a computer, we can easily record how users respond to the information with which they are presented. These normal interactions between users and information systems are implicit feedback. The key question we address is -- how can we use implicit feedback to automatically improve interactive information systems, such as desktop search and Web search? Contrasting with data collected from external experts, which is assumed as input in most previous research on optimizing interactive information systems, implicit feedback gives more accurate and up-to-date data about the needs of actual users. While another alternative is to ask users for feedback directly, implicit feedback collects data from all users, and does not require them to change how they interact with information systems. What makes learning from implicit feedback challenging, is that the behavior of people using interactive information systems is strongly biased in several ways. These biases can obscure the useful information present, and make standard machine learning approaches less effective. This thesis shows that implicit feedback provides a tremendous amount of practical information for learning to rank, making four key contributions. First, we demonstrate that query reformulations can be interpreted to provide relevance information about documents that are presented to users. Second, we describe an experiment design that provably avoids presentation bias, which is otherwise present when recording implicit feedback. Third, we present a Bayesian method for collecting more useful implicit feedback for learning to rank, by actively selecting rankings to show in anticipation of user responses. Fourth, we show how to learn rankings that resolve query ambiguity using multi-armed bandits. Taken together, these contributions reinforce the value of implicit feedback, and present new ways it can be exploited