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 …

This paper outlines a conceptual framework for understanding recent developments in
information retrieval and natural language processing that attempts to integrate dense and …

Semantic text matching is one of the most important research problems in many domains,
including, but not limited to, information retrieval, question answering, and recommendation …

Estimating the similarity between two legal case documents is an important and challenging
problem, having various downstream applications such as prior-case retrieval and citation …

We propose the Neural Vector Space Model (NVSM), a method that learns representations
of documents in an unsupervised manner for news article retrieval. In the NVSM paradigm …

The Transformer-Kernel (TK) model has demonstrated strong reranking performance on the
TREC Deep Learning benchmark---and can be considered to be an efficient (but slightly …

Recommender systems can support everyday digital tasks by retrieving and recommending
useful information contextually. This is becoming increasingly relevant in services and …

Personal knowledge about users' professions, hobbies, favorite food, and travel
preferences, among others, is a valuable asset for individualized AI, such as recommenders …

Machine learning plays a role in many aspects of modern IR systems, and deep learning is
applied in all of them. The fast pace of modern-day research has given rise to many different …

While great strides have been made in the field of search and recommendation, there are
still challenges and opportunities to address information access issues that involve solving …