Machine Learning Methods and Models for Ranking

Ranking problems are ubiquitous and occur in a variety of domains that include social choice, information retrieval, computational biology and many others. Recent advancements in information technology have opened new data processing possibilities and significantly increased the complexity of computationally feasible methods. Through these advancements ranking models are now beginning to be applied to many new and diverse problems. Across these problems data, which ranges from gene expressions to images and web-documents, has vastly different properties and is often not human generated. This makes it challenging to apply many of the existing models for ranking which primarily originate in social choice and are typically designed for human generated preference data. As the field continues to evolve a new trend has recently emerged where machine learning methods are being used to automatically learn the ranking models. While these methods typically lack the theoretical support of the social choice models they often show excellent empirical performance and are able to handle large and diverse data placing virtually no restrictions on the data type. These model have now been successfully applied to many diverse ranking problems including image retrieval, protein selection, machine translation and many others. Inspired by these promising results the work presented in this thesis aims to advance machine methods for ranking and develop new techniques to allow effective modeling of existing and future problems. The presented work concentrates on three different but related domains: information retrieval, preference aggregation and collaborative filtering. In each domain we develop new models together with learning and inference methods and empirically verify our models on real-life data.