Ranking Model Learning Task

A Ranking Model Learning Task is a model learning task that can learn a Ranking Model.

• AKA: Rank Function Learning.
• Context:
  • It can be solved by a Ranking Model Learning Sytem (that implements a Ranking Model Learning Algorithm).
  • …
• Counter-Example(s):
  • Classification Model Learning Task.
  • Regression Model Learning Task.
• See: Ranking Performance Measure.

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References

2008

• (Chakrabarti et al., 2008) ⇒ Soumen Chakrabarti, Rajiv Khanna, Uma Sawant, and Chiru Bhattacharyya. (2008). “Structured Learning for Non-smooth Ranking Losses.” In: Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2008). doi:10.1145/1401890.1401906
  • QUOTE: Learning to rank from relevance judgment is an active research area. Itemwise score regression, pairwise preference satisfaction, and listwise structured learning are the major techniques in use. Listwise structured learning has been applied recently to optimize important non-decomposable ranking criteria like AUC (area under ROC curve) and MAP (mean average precision). We propose new, almost-linear-time algorithms to optimize for two other criteria widely used to evaluate search systems: MRR (mean reciprocal rank) and NDCG (normalized discounted cumulative gain) in the max-margin structured learning framework.

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