Rankings are the primary interface through which many online platforms match users to
items (eg news, products, music, video). In these two-sided markets, not only the users draw …

Implicit feedback (eg, clicks, dwell times, etc.) is an abundant source of data in human-
interactive systems. While implicit feedback has many advantages (eg, it is inexpensive to …

Abstract Conventional Learning-to-Rank (LTR) methods optimize the utility of the rankings to
the users, but they are oblivious to their impact on the ranked items. However, there has …

Rankings are ubiquitous in the online world today. As we have transitioned from finding
books in libraries to ranking products, jobs, job applicants, opinions and potential romantic …

Rankings of people and items are at the heart of selection-making, match-making, and
recommender systems, ranging from employment sites to sharing economy platforms. As …

In the past few years, there has been much work on incorporating fairness requirements into
algorithmic rankers, with contributions coming from the data management, algorithms …

Ranking items by their probability of relevance has long been the goal of conventional
ranking systems. While this maximizes traditional criteria of ranking performance, there is a …

Addressing unfairness in rankings has become an increasingly important problem due to the
growing influence of rankings in critical decision making, yet existing learning-to-rank …

Rankings, especially those in search and recommendation systems, often determine how
people access information and how information is exposed to people. Therefore, how to …

A machine learning approach to learning to rank trains a model to optimize a target
evaluation measure with repect to training data. Currently, existing information retrieval …