EGRank: An exponentiated gradient algorithm for sparse learning-to-rank
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 ℓ1...
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01.10.2018
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| 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 ℓ1 constraint. Our proposed algorithm has a competitive theoretical worst-case performance guarantee, that is, we can obtain an ϵ-accurate solution after O(1ϵ) 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. |
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| AbstractList | 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 ℓ1 constraint. Our proposed algorithm has a competitive theoretical worst-case performance guarantee, that is, we can obtain an ϵ-accurate solution after O(1ϵ) 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. |
| Author | Huang, Changqin Du, Lei Ding, Jintang Lai, Hanjiang Pan, Yan |
| Author_xml | – sequence: 1 givenname: Lei surname: Du fullname: Du, Lei organization: School of Data and Computer Science, Sun Yat-sen University, Guangzhou, China – sequence: 2 givenname: Yan orcidid: 0000-0002-0466-3763 surname: Pan fullname: Pan, Yan email: panyan5@mail.sysu.edu.cn organization: School of Data and Computer Science, Sun Yat-sen University, Guangzhou, China – sequence: 3 givenname: Jintang surname: Ding fullname: Ding, Jintang organization: School of Data and Computer Science, Sun Yat-sen University, Guangzhou, China – sequence: 4 givenname: Hanjiang surname: Lai fullname: Lai, Hanjiang organization: School of Data and Computer Science, Sun Yat-sen University, Guangzhou, China – sequence: 5 givenname: Changqin orcidid: 0000-0003-1371-2608 surname: Huang fullname: Huang, Changqin organization: School of Information Technology in Education, South China Normal University, Guangzhou, China |
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| Title | EGRank: An exponentiated gradient algorithm for sparse learning-to-rank |
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