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 …

Recommender systems are the algorithms which select, filter, and personalize content
across many of the world's largest platforms and apps. As such, their positive and negative …

A recent “third wave” of neural network (NN) approaches now delivers state-of-the-art
performance in many machine learning tasks, spanning speech recognition, computer …

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 …

Many online services, such as search engines, social media platforms, and digital
marketplaces, are advertised as being available to any user, regardless of their age, gender …

Getting a better understanding of user behavior is important for advancing information
retrieval systems. Existing work focuses on modeling and predicting single interaction …

Click-based learning to rank (LTR) tackles the mismatch between click frequencies on items
and their actual relevance. The approach of previous work has been to assume a model of …

The SIGIR 2016 workshop on Neural Information Retrieval (Neu-IR) took place on 21 July,
2016 in Pisa. The goal of the Neu-IR (pronounced" New IR") workshop was to serve as a …

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 …

Click-through information is considered as a valuable source of users' implicit relevance
feedback for commercial search engines. As existing studies have shown that the search …