We are organizing the "Learning From Noisy/Limited Data for Information Retrieval" workshop which is co-located with SIGIR 2018. This is the first edition of this workshop and The goal of the workshop is to bring together researchers from industry, where data is plentiful but noisy, with researchers from academia, where data is sparse but clean, to discuss solutions to these related problems.
We invited contributions relevant to this topics:
- Learning from noisy data for IR
- Learning from automatically constructed data
- Learning from implicit feedback data, e.g., click data
- Distant or weak supervision and learning from IR heuristics
- Unsupervised and semi-supervised learning for IR
- Transfer learning for IR
- Incorporating expert/domain knowledge to improve learning-based IR models
- Learning from labeled features
- Incorporating IR axioms to improve machine learning models
Marc Najork is going to give a fantastic keynote on "Using biased data for learning-to-rank" and we have a set of fantastic papers (including mine :P) that are going to be presented at the workshop and a great discussion panel with wonderful panelist from both industry and academia.
Save the date on your calendar!