The problem of ranking, in which the goal is to learn an ordering or ranking
over objects, has recently gained much attention in machine learning.
Progress has been made in formulating different forms of the ranking
problem, proposing and analyzing algorithms for these forms, and developing
theory for them. However, a multitude of basic questions remain unanswered:
This workshop aims to provide a forum for discussion and debate among
researchers interested in the topic of ranking, with a focus on the basic
questions above. The goal is not to find immediate answers, but rather to
discuss possible methods and applications, develop intuition, brainstorm on
possible directions and, in the process, encourage dialogue and
collaboration among researchers with complementary ideas.
Ranking problems may differ in many ways: in the form of the training
examples, in the form of the desired output, and in the performance measure
used to evaluate success. What are the consequences of each of these factors
on the design of ranking algorithms and on their theoretical guarantees?
The relationships between ranking and other classical learning
problems such as classification and regression are still under-explored. Is
any of these problems inherently harder or easier than another?
Although ranking is studied mainly as a supervised learning problem, it can
have important consequences for other forms of learning; for example, in
semi-supervised learning, one often ranks unlabeled examples so as to assign
labels to the ones ranked at the top, and in reinforcement learning, one
often learns a policy that ranks actions for each state. To what extent can
these connections be explored and exploited?
There is a large variety of applications in which ranking is required,
ranging from information retrieval to collaborative filtering to
computational biology. What forms of ranking are most suited to different
applications? What are novel applications that can benefit from ranking,
and what other forms of ranking do these applications point us to?