About
I am a researcher at the Machine Intelligence and Perception group at Microsoft Research Cambridge. I am the research lead of Project Malmo, which uses the popular game Minecraft as an experimentation platform for developing intelligent technology. My long-term goal is to develop AI systems that learn to collaborate with people, to empower their users and help solve complex real-world problems.
Outside of Project Malmo, I work on online evaluation and interactive learning for information retrieval. This means that I try to understand how we can apply machine learning an artificial intelligence to develop more intelligent search and recommendation systems.
Before joining Microsoft Research, I completed my PhD in Computer Science as part of the ILPS group at the University of Amsterdam. I worked with Maarten de Rijke and Shimon Whiteson on smart search engines that learn directly from their users. My thesis on Fast and Reliable Online Learning to Rank in Information Retrieval can be downloaded from my personal homepage.
Publications
2016
The Malmo Platform for Artificial Intelligence Experimentation
Matthew Johnson, Katja Hofmann, Tm Hutton, David Bignell, in 25th International Joint Conference on Artificial Intelligence (IJCAI-16), AAAI - Association for the Advancement of Artificial Intelligence, July 12, 2016, View abstract, Download PDFOnline Evaluation for Information Retrieval
Katja Hofmann, Lihong Li, Filip Radlinski, in Foundations and Trends® in Information Retrieval, Now Publishers, June 22, 2016, View abstract, Download PDF, View external linkTowards Conversational Recommender Systems
Konstantina Christakopoulou, Filip Radlinski, Katja Hofmann, in 22nd ACM SigKDD Conference on Knowledge Discovery and Data Mining, ACM – Association for Computing Machinery, June 9, 2016, View abstract, Download PDFCollective Noise Contrastive Estimation for Policy Transfer Learning
Weinan Zhang, Ulrich Paquet, Katja Hofmann, in Thirtieth AAAI Conference on Artificial Intelligence (AAAI 2016), AAAI - Association for the Advancement of Artificial Intelligence, February 21, 2016, View abstract, Download PDF, View external link2015
Predicting Search Satisfaction Metrics with Interleaved Comparisons
Anne Schuth, Katja Hofmann, Filip Radlinski, in Proceedings of the ACM International Conference on Research and Development in Information Retrieval (SIGIR), ACM – Association for Computing Machinery, August 1, 2015, View abstract, Download PDFContextual Dueling Bandits
Miro Dudík, Katja Hofmann, Robert Schapire, Alex Slivkins, Masrour Zoghi, in Proceedings of The 28th Conference on Learning Theory (COLT), July 1, 2015, View abstract, Download PDFOnline Learning to Rank: Absolute vs. Relative
Yiwei Chen, Katja Hofmann, in Proceedings of the 24th international conference on World Wide Web (WWW), ACM – Association for Computing Machinery, May 20, 2015, View abstract, Download PDF, View external link2014
An Eye-tracking Study of User Interactions with Query Auto Completion
Katja Hofmann, Bhaskar Mitra, Filip Radlinski, Milad Shokouhi, in Proceedings of the 23rd ACM International Conference on Information and Knowledge Management (CIKM), ACM – Association for Computing Machinery, November 1, 2014, View abstract, Download PDF, View external linkOn User Interactions with Query Auto-Completion
Bhaskar Mitra, Milad Shokouhi, Filip Radlinski, Katja Hofmann, in Proceedings of the ACM International Conference on Research and Development in Information Retrieval (SIGIR), ACM, July 1, 2014, View abstract, Download PDFEffects of Position Bias on Click-Based Recommender Evaluation
Katja Hofmann, Anne Schuth, Alejandro Bellogin, Maarten de Rijke, in 36th European Conference on Information Retrieval (ECIR'14), Springer, January 1, 2014, View abstract, Download PDF2013
Lerot: An online learning to rank framework
Anne Schuth, Katja Hofmann, Shimon Whiteson, Maarten de Rijke, in Living Lab'13: Workshop on Living Labs for Information Retrieval Evaluation, ACM, January 1, 2013, View abstract, Download PDFProjects
Link description
Query formulation with Query Auto Completion
Date
November 18, 2014
Downloads
Project Malmo on GitHub
July 2016
Project Malmo is a platform for Artificial Intelligence experimentation and research built on top of Minecraft. We aim to inspire a new generation of research into challenging new problems presented by this unique environment.
- Github
Click the icon to access this download
Other
For a list of my publications before joining MSR, please see the ILPS (Information and Language Processing Systems) list of publications, MSR Academic, or dblp.