In the talk I will discuss work on learning to rank for information retrieval, in which the goal is to automatically construct a model that ranks documents in response to a query. In traditional supervised machine learning approaches for the LTR problem one manually selects a set of manually engineered ranking features and then learns the best way of combining them to obtain the most powerful ranking model that those features are capable of producing. In ongoing work on truly autonomous search engines, we are moving evaluation, learning and feature engineering to a weakly supervised paradigm, learning from the implicit feedback that naturally emerges as part of users' interactions with the search engine. I will discuss recent progress in each of these three dimensions: evaluation, learning and feature engineering.
Maarten de Rijke (University of Amsterdam)