Pairwise learning to rank by neural networks revisited: reconstruction, theoretical analysis and practical performance

Marius Köppel, Alexander Segner, Martin Wagener, Lukas Pensel, Andreas Karwath, Stefan Kramer

Research output: Contribution to conference (unpublished)Paperpeer-review

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Abstract

We present a pairwise learning to rank approach based on a neural net, called DirectRanker, that generalizes the RankNet architecture. We show mathematically that our model is reflexive, antisymmetric, and transitive allowing for simplified training and improved performance. Experimental results on the LETOR MSLR-WEB10K, MQ2007 and MQ2008 datasets show that our model outperforms numerous state-of- the-art methods, while being inherently simpler in structure and using a pairwise approach only.
Original languageEnglish
Publication statusPublished - 16 Sept 2019
EventEuropean Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases - Würzburg, Germany, Würzburg, Germany
Duration: 16 Sept 201920 Sept 2019
https://ecmlpkdd2019.org

Conference

ConferenceEuropean Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases
Abbreviated titleECMLPKDD
Country/TerritoryGermany
CityWürzburg
Period16/09/1920/09/19
Internet address

Keywords

  • Information Retrieval
  • Machine Learning
  • Learning to Rank

ASJC Scopus subject areas

  • Artificial Intelligence
  • Information Systems

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