Back to Search
Start Over
EGRank: An exponentiated gradient algorithm for sparse learning-to-rank.
- Source :
-
Information Sciences . Oct2018, Vol. 467, p342-356. 15p. - Publication Year :
- 2018
-
Abstract
- Abstract This paper focuses on the problem of sparse learning-to-rank , where the learned ranking models usually have very few non-zero coefficients. An exponential gradient algorithm is proposed to learn sparse models for learning-to-rank, which can be formulated as a convex optimization problem with the ℓ 1 constraint. Our proposed algorithm has a competitive theoretical worst-case performance guarantee, that is, we can obtain an ϵ-accurate solution after O (1 ϵ) iterations. An early stopping criterion based on Fenchel duality is proposed to make the algorithm be more efficient in practice. Extensive experiments are conducted on some benchmark datasets. The results demonstrate that a sparse ranking model can significantly improve the accuracy of ranking prediction compared to dense models, and the proposed algorithm shows stable and competitive performance compared to several state-of-the-art baseline algorithms. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 00200255
- Volume :
- 467
- Database :
- Academic Search Index
- Journal :
- Information Sciences
- Publication Type :
- Periodical
- Accession number :
- 131730268
- Full Text :
- https://doi.org/10.1016/j.ins.2018.07.043