Learning to Rank

Learning to Rank#

Student seminar — Session 22

Focus#

Learning to rank: problem settings (pointwise, pairwise, listwise), evaluation metrics (Precision@k, MAP, NDCG), and representative methods.

Reading (examples)#

  • Zaki & Meira, ranking / evaluation chapters

  • Liu, Learning to Rank for Information Retrieval (selected sections)

  • A recent survey or application paper (to be assigned)

Student tasks#

  • Explain the problem formalization and assumptions

  • Present one method and its objective/loss

  • Discuss evaluation protocol and limitations

  • Q&A and short discussion

Contents