Projects per year
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.
|Publication status||Published - 16 Sept 2019|
|Event||European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases - Würzburg, Germany, Würzburg, Germany|
Duration: 16 Sept 2019 → 20 Sept 2019
|Conference||European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases|
|Period||16/09/19 → 20/09/19|
- Information Retrieval
- Machine Learning
- Learning to Rank
ASJC Scopus subject areas
- Artificial Intelligence
- Information Systems
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- 1 Finished
Andreas Karwath - Semantic-based secure infrastructure for interoperable, in-depth biomedical data and analytics
Cazier, J., Karwath, A., Yau, C. & Gkoutos, G.
14/02/18 → 13/02/21
Project: Research Councils