Of particular interest is the Learning to Rank for Information Retrieval workshop. Papers are due by June 8th. From the description:
The task of "learning to rank" has emerged as an active and growing area of research both in information retrieval and machine learning. The goal is to design and apply methods to automatically learn a function from training data, such that the function can sort objects (e.g., documents) according to their degrees of relevance, preference, or importance as defined in a specific application.
The relevance of this task for IR is without question, because many IR problems are by nature ranking problems. Improved algorithms for learning ranking functions promise improved retrieval quality and less of a need for manual parameter adaptation. In this way, many IR technologies can be potentially enhanced by using learning to rank techniques.
A major theme at the workshop will be of course, LETOR, MSR Asia's collection of datasets to compare these type of machine learning based ranking systems. See my previous post on LETOR.
The LETOR website now has some critical bug fixes posted on the first version and a formal release is planned for the end of the month (according to the website).