“Deep Learning with Logged Bandit Feedback” by Thorsten Joachims, Adith Swaminathan and Maarten de Rijke, to be published at ICLR 2018, is available online. In the paper we propose a new output layer for deep neural networks that permits the use of logged contextual bandit feedback for training. Such contextual bandit feedback can be available…
WWW 2018 paper on Manifold Learning for Rank Aggregation online
“Manifold Learning for Rank Aggregation” by Shangsong Liang, Ilya Markov, Zhaochun Ren, and Maarten de Rijke, which will be published at WWW 2018, is available online now. In the paper we address the task of fusing ranked lists of documents that are retrieved in response to a query. Past work on this task of rank…
JASIST paper “The birth of collective memories: Analyzing emerging entities in text streams” online
“The birth of collective memories: Analyzing emerging entities in text streams” by David Graus, Daan Odijk and Maarten de Rijke, to be published in the Journal of the Association for Information Science and Technology is online now at this location. In the paper we study how collective memories are formed online. We do so by…
WSDM 2018 paper on Why People Search for Images using Web Search Engines online
“Why People Search for Images using Web Search Engines” by Xiaohui Xie, Yiqun Liu, Maarten de Rijke, Jiyin He, Min Zhang and Shaoping Ma is online now at this location. It will be published at WSDM 2018. What are the intents or goals behind human interactions with image search engines? Knowing why people search for…
IRJ paper “Neural information retrieval: at the end of the early years” online
Our Information Retrieval Journal paper “Neural information retrieval: at the end of the early years” by Kezban Dilek Onal, Ye Zhang, Ismail Sengor Altingovde, Md Mustafizur Rahman, Pinar Karagoz, Alex Braylan, Brandon Dang, Heng-Lu Chang, Henna Kim, Quinten McNamara, Aaron Angert, Edward Banner, Vivek Khetan, Tyler McDonnell, An Thanh Nguyen, Dan Xu, Byron C. Wallace,…
CIKM 2018 papers online
Our CIKM 2017 papers are online now: “Online expectation-maximization for click models” by Ilya Markov, Alexey Borisov, and Maarten de Rijke. Click models allow us to interpret user click behavior in search interactions and to remove various types of bias from user clicks. Existing studies on click models consider a static scenario where user click…
FAT* Conference on Fairness, Accountability, and Transparency
FAT* is a multi-disciplinary conference that brings together researchers and practitioners interested in fairness, accountability, and transparency in socio-technical systems. Artificial intelligence, automation, and machine learning are being adopted in a growing number of contexts. Fueled by big data, these systems filter, sort, score, recommend, personalize, and otherwise shape human experiences of socio-technical systems. Although…
Material from NN4IR tutorial online
The material from our highly popular tutorial on Neural Networks for Information Retrieval (NN4IR), presented during SIGIR 2017 in Tokyo is available online at http://nn4ir.com.
Neural Networks for Information Retrieval tutorial at SIGIR 2017
Title: Neural Networks for Information Retrieval (NN4IR) Description: Machine learning plays an important role in many aspects of modern IR systems, and deep learning is applied to all of those. The fast pace of modern-day research into deep learning has given rise to many different approaches to many different IR problems. What are the underlying…
Neu-IR: The SIGIR 2017 Workshop on Neural Information Retrieval
After a very successful first edition of Neu-IR at SIGIR 2016, we are happy to organize a second version of the workshop at SIGIR 2017. Key facts: the web site for Neu-IR 2017 is online now at http://neu-ir.weebly.com, the deadline for submissions is June 11, 2017 and the workshop itself will take place on Friday…