Siks page: Advances in Information Retrieval course, 2019.

October 7th

  1. Claudia Hauff, “Machine Learning for IR”; .pdf, @google;
  2. Hinda Haned, “Explainable and fair machine learning”; .pdf, @github;
  3. Faegheh Hasibi, “Knowledge graphs & semantic search”; .pdf;
  4. Arjen P. de Vries, “Siks Query Variants Experiment” (part 1). .pdf, assignment.

October 8th

  1. Djoerd Hiemstra, “Managing terabytes: A Google-sized search index”; .pdf;
  2. Suzan Verberne, “Evaluation and user models”; .pdf;
  3. Djoerd Hiemstra, “Siks Query Variants Experiment” (part 2); assignment;
  4. Mostafa Dehghani, “Learning with Imperfect Supervision”; .pdf;
  5. Dimitrios Rafailidis, “Cross-domain Recommendation”; .pdf;
  6. David Graus, “Bias in recommendation”; .pdf, @slideshare.

Practical Assignment

As Siks PhD students in the information retrieval course, we will investigate the effect of query formulation on retrieval performance.

Inspiration / background reading: a recent query variations paper by Shane Culpepper; the UQV 100 dataset; classic paper on rank fusion.

Experiments using the OSIRRC jig.

Pick your team and go!


Results so far are summarised in the slides.

Evaluation Assignment

Two versions of the “Implementing IR Evaluation Measures” Notebook: