The advent of deep machine learning platforms such as Tensorflow and Pytorch, developed
in expressive high-level languages such as Python, have allowed more expressive …

Search engines are exceptionally important tools for accessing information in today's world.
In satisfying the information needs of millions of users, the effectiveness (the quality of the …

Complex machine learning models are now an integral part of modern, large-scale retrieval
systems. However, collection size growth continues to outpace advances in efficiency …

Modern multi-stage retrieval systems are comprised of a candidate generation stage
followed by one or more reranking stages. In such an architecture, the quality of the final …

Large scale retrieval systems often employ cascaded ranking architectures, in which an
initial set of candidate documents are iteratively refined and re-ranked by increasingly …

Reducing excessive costs in feature acquisition and model evaluation has been a long-
standing challenge in learning-to-rank systems. A cascaded ranking architecture turns …

We propose a method that integrates relevance and understandability to rank health web
documents. We use a learning to rank approach with standard retrieval features to …

Rank fusion is a powerful technique that allows multiple sources of information to be
combined into a single result set. Query variations covering the same information need …

Research activity spanning more than five decades has led to index organizations,
compression schemes, and traversal algorithms that allow extremely rapid response to …

Modern Information Retrieval (IR) systems have become more and more complex, involving
a large number of parameters. For example, a system may choose from a set of possible …