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.
l Models, features, and algorithms of learning to rank
l Evaluation methods for learning to rank
l Data creation methods for learning to rank
l Applications of learning to rank methods to information retrieval
l Comparison between traditional approaches and learning approaches to ranking
l Theoretical analyses on learning to rank
l Empirical comparison between learning to rank methods
