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 …

Equip** machines with comprehensive knowledge of the world's entities and their
relationships has been a longstanding goal of AI. Over the last decade, large-scale …

Text retrieval is a long-standing research topic on information seeking, where a system is
required to return relevant information resources to user's queries in natural language. From …

The goal of text ranking is to generate an ordered list of texts retrieved from a corpus in
response to a query. Although the most common formulation of text ranking is search …

Recent years have witnessed the emerging success of graph neural networks (GNNs) for
modeling structured data. However, most GNNs are designed for homogeneous graphs, in …

This work proposes a novel adaptation of a pretrained sequence-to-sequence model to the
task of document ranking. Our approach is fundamentally different from a commonly …

We present a new technique for learning visual-semantic embeddings for cross-modal
retrieval. Inspired by hard negative mining, the use of hard negatives in structured …

The advent of deep neural networks pre-trained via language modeling tasks has spurred a
number of successful applications in natural language processing. This work explores one …

Ranking models lie at the heart of research on information retrieval (IR). During the past
decades, different techniques have been proposed for constructing ranking models, from …

Item recommendation is a personalized ranking task. To this end, many recommender
systems optimize models with pairwise ranking objectives, such as the Bayesian …