Advances in IR Siks PhD Course
Siks page: Advances in Information Retrieval course, 2019.
- Claudia Hauff, “Machine Learning for IR”; .pdf, @google;
- Hinda Haned, “Explainable and fair machine learning”; .pdf, @github;
- Faegheh Hasibi, “Knowledge graphs & semantic search”; .pdf;
- Arjen P. de Vries, “Siks Query Variants Experiment” (part 1). .pdf, assignment.
- Djoerd Hiemstra, “Managing terabytes: A Google-sized search index”; .pdf;
- Suzan Verberne, “Evaluation and user models”; .pdf;
- Djoerd Hiemstra, “Siks Query Variants Experiment” (part 2); assignment;
- Mostafa Dehghani, “Learning with Imperfect Supervision”; .pdf;
- Dimitrios Rafailidis, “Cross-domain Recommendation”; .pdf;
- David Graus, “Bias in recommendation”; .pdf, @slideshare.
As Siks PhD students in the information retrieval course, we will investigate the effect of query formulation on retrieval performance.
Experiments using the OSIRRC
Pick your team and go!
Results so far are summarised in the slides.
Two versions of the “Implementing IR Evaluation Measures” Notebook: