This paper provides an extensive and thorough overview of the models and techniques
utilized in the first and second stages of the typical information retrieval processing chain …

As language models become more powerful, training and evaluation are increasingly
bottlenecked by the data and metrics used for a particular task. For example, summarization …

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 …

Neural ranking models for information retrieval (IR) use shallow or deep neural networks to
rank search results in response to a query. Traditional learning to rank models employ …

Objective assessment of image quality is fundamentally important in many image processing
tasks. In this paper, we focus on learning blind image quality assessment (BIQA) models …

A recent line of works apply machine learning techniques to assist or rebuild cost-based
query optimizers in DBMS. While exhibiting superiority in some benchmarks, their …

As recently as the 1990s, studies showed that most people preferred getting information
from other people rather than from information retrieval systems. Of course, in that time …

Information retrieval (IR) has changed considerably in recent years with the expansion of the
World Wide Web and the advent of modern and inexpensive graphical user interfaces and …

Learning to rank for Information Retrieval (IR) is a task to automatically construct a ranking
model using training data, such that the model can sort new objects according to their …

This paper presents an approach to automatically optimizing the retrieval quality of search
engines using clickthrough data. Intuitively, a good information retrieval system should …