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 …

Ranking algorithms are deployed widely to order a set of items in applications such as
search engines, news feeds, and recommendation systems. Recent studies, however, have …

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 …

There has been surge in the usage of Internet as well as social media platforms which has
led to rise in online hate speech targeted on individual or group. In the recent years, hate …

The goal in extreme multi-label classification (XMC) is to learn a classifier which can assign
a small subset of relevant labels to an instance from an extremely large set of target labels …

Person-job fit is the core technique of online recruitment platforms, which can improve the
efficiency of recruitment by accurately matching the job positions with the job seekers …

Ranking in information retrieval has been traditionally approached as a pursuit of relevant
information, under the assumption that the users' information needs are unambiguously …

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 …

Interleaving is an increasingly popular technique for evaluating information retrieval systems
based on implicit user feedback. While a number of isolated studies have analyzed how this …

Information networks are widely used to characterize the relationships between data items
such as text documents. Many important retrieval and mining tasks rely on ranking the data …