A cross-benchmark comparison of 87 learning to rank methods

By Niek Tax, Sander Bockting, Djoerd Hiemstra. 2015

90 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. J. Shane Culpepper
2. Roi Blanco
3. Ruey-Cheng Chen
4. Luke Gallagher
5. Jamie Callan
6. Zhuyun Dai

In Information Systems

1. J. Shane Culpepper
2. Roi Blanco
3. Ruey-Cheng Chen
4. Luke Gallagher

Citations in the corpus

• Off-policy evaluation for slate recommendation (2016, neutral): “We use the MSLR-WEB10K dataset [30] to compare our approach with benchmarked results [33] for NDCG@3 (i.”
• Efficient Cost-Aware Cascade Ranking in Multi-Stage Retrieval (2017, methodology): “� is cascade model, however, cannot be used with gradient-boosted tree models, which are now widely believed to be state-of-the art for web search ranking algorithms [25, 30].”
• Quality versus efficiency in document scoring with learning-to-rank models (2016, unknown): “propose a comparison of several LtR algorithms [10] by focusing on quality issues and without 85”