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 …

Is neural IR mostly hype? In a recent SIGIR Forum article, Lin expressed skepticism that
neural ranking models were actually improving ad hoc retrieval effectiveness in limited data …

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 …

Recently, we have witnessed the bloom of neural ranking models in the information retrieval
(IR) field. So far, much effort has been devoted to developing effective neural ranking …

Learning to rank has been intensively studied and widely applied in information retrieval.
Typically, a global ranking function is learned from a set of labeled data, which can achieve …

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 …

Despite the impressive improvements achieved by unsupervised deep neural networks in
computer vision and NLP tasks, such improvements have not yet been observed in ranking …

Many machine learning technologies such as support vector machines, boosting, and neural
networks have been applied to the ranking problem in information retrieval. However, since …

Recently, neural networks have been successfully employed to improve upon state-of-the-
art effectiveness in ad-hoc retrieval tasks via machine-learned ranking functions. While …

Many ranking models have been proposed in information retrieval, and recently machine
learning techniques have also been applied to ranking model construction. Most of the …