Machine Learning (ML) forms the basis of many Information Retrieval (IR) technologies, ranging from early work on text classification to recent approaches to entity linking, sentiment detection, and document ranking. In addition to serving as a key application area for ML, IR continuously pushes ML towards novel approaches.
In this talk I discuss and exemplify the dual role of IR as both a consumer of ML technology, and as a driver towards new challenging ML problems. I start with an overview of typical ML applications to IR, including an overview of learning to rank approaches. In the second part of the lecture I focus on a recent trend towards online learning approaches that allow continuous learning from user interactions. I discuss existing solutions, and conclude by highlighting open questions and directions for future research.
Lecturer: Dr. Katja Hofmann
