Advances in Ranking

Ranking problems are increasingly recognized as a new class of statistical learning problems that are distinct from the classical learning problems of classification and regression. Such problems arise in a wide variety of domains: in information retrieval, one wants to rank documents according to relevance to a query; in natural language processing, one wants to rank alternative parses or translations of a sentence; in collaborative filtering, one wants to rank items according to a user's likes and dislikes; in computational biology, one wants to rank genes according to relevance to a disease. Consequently, there has been much interest in ranking in recent years, with a variety of methods being developed and a whole host of new applications being discovered.

This workshop aims to bring together researchers interested in the area to share their perspectives, identify persisting challenges as well as opportunities for meaningful dialogue and collaboration, and to discuss possible directions for further advances and applications in the future.

One of the primary goals of the workshop will be to reach out to a broad audience. To this end, we will have talks on topics ranging from more statistically/mathematically oriented approaches to ranking, to newer application areas. A second goal will be to bring to the fore a range of questions that are currently being debated within the community, for example via a panel discussion between experts in the field.

Overall, the workshop will aim to provide a forum that showcases recent advances in ranking to the broader community, facilitates open debate on some of the questions in this area, and helps catalyze further interest among those new to the topic.