Ranking Model Learning Algorithm

A Ranking Model Learning Algorithm is a model-based learning algorithm that can learn a ranking model.

• AKA: Ranking Function Learning Algorithm.
• Context:
  • It can be applied by a Ranking Model Learning System (to solve a Ranking Model Learning Task).
• Example(s):
  • AdaRank.
  • RankBoost.
  • Ranking SVM.
  • …
• Counter-Example(s):
  • Classification Model Learning Algorithm.
• See: Ranking Performance Measure.

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References

2007

• (Xu & Li, 2007) ⇒ Jun Xu, and Hang Li. (2007). “AdaRank: A Boosting Algorithm for Information Retrieval.” In: Proceedings of the 30th annual international ACM SIGIR conference http://doi]acm.org/10.1145/1277741.1277809 doi:10.1145/1277741.1277809]].
  • QUOTE: Several methods for learning to rank have been developed and applied to document retrieval. For example, Herbrich et al. [13] propose a learning algorithm for ranking on the basis of Support Vector Machines, called Ranking SVM. Freund et al. [8] take a similar approach and perform the learning by using boosting, referred to as RankBoost.

2005

• (Burges et al., 2005) ⇒ Chris Burges, Tal Shaked, Erin Renshaw, Ari Lazier, Matt Deeds, Nicole Hamilton, and Greg Hullender. (2005). “Learning to Rank Using Gradient Descent.” In: Proceedings of the 22nd International Conference on Machine learning (ICML 2005) doi:10.1145/1102351.1102363.
  • ABSTRACT: We investigate using gradient descent methods for learning ranking functions; we propose a simple probabilistic cost function, and we introduce RankNet, an implementation of these ideas using a neural network to model the underlying ranking function. We present test results on toy data and on data from a commercial internet search engine.

2003

• (Brazdil et al., 2003) ⇒ Pavel B. Brazdil, Carlos Soares, and Joaquim Pinto da Costa1. (2003). “Ranking Learning Algorithms: Using IBL and Meta-Learning on Accuracy and Time Results.” In: Machine Learning, 50(3). doi:10.1023/A:1021713901879

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