An introduction to neural information retrieval
B Mitra, N Craswell - Foundations and Trends® in Information …, 2018 - nowpublishers.com
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 …
rank search results in response to a query. Traditional learning to rank models employ …
Building human values into recommender systems: An interdisciplinary synthesis
J Stray, A Halevy, P Assar, D Hadfield-Menell… - ACM Transactions on …, 2024 - dl.acm.org
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 …
across many of the world's largest platforms and apps. As such, their positive and negative …
Neural information retrieval: At the end of the early years
KD Onal, Y Zhang, IS Altingovde, MM Rahman… - Information Retrieval …, 2018 - Springer
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 …
performance in many machine learning tasks, spanning speech recognition, computer …
Neural vector spaces for unsupervised information retrieval
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 …
of documents in an unsupervised manner for news article retrieval. In the NVSM paradigm …
Auditing search engines for differential satisfaction across demographics
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 …
marketplaces, are advertised as being available to any user, regardless of their age, gender …
A click sequence model for web search
Getting a better understanding of user behavior is important for advancing information
retrieval systems. Existing work focuses on modeling and predicting single interaction …
retrieval systems. Existing work focuses on modeling and predicting single interaction …
Reaching the end of unbiasedness: Uncovering implicit limitations of click-based learning to rank
H Oosterhuis - Proceedings of the 2022 ACM SIGIR International …, 2022 - dl.acm.org
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 …
and their actual relevance. The approach of previous work has been to assume a model of …
Report on the sigir 2016 workshop on neural information retrieval (neu-ir)
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 …
2016 in Pisa. The goal of the Neu-IR (pronounced" New IR") workshop was to serve as a …
Neural networks for information retrieval
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 …
applied in all of them. The fast pace of modern-day research has given rise to many different …
Time-aware click model
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 …
feedback for commercial search engines. As existing studies have shown that the search …