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- W2089918128 abstract "Recently, learning-to-rank has attracted considerable attention. Although significant research efforts have been focused on learning-to-rank, it is not the case for the problem of learning sparse models for ranking. In this paper, we consider the sparse learning-to-rank problem. We formulate it as an optimization problem with the ℓ1 regularization, and develop a simple but efficient iterative algorithm to solve the optimization problem. Experimental results on four benchmark datasets demonstrate that the proposed algorithm shows (1) superior performance gain compared to several state-of-the-art learning-to-rank algorithms, and (2) very competitive performance compared to FenchelRank that also learns a sparse model for ranking." @default.
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- W2089918128 date "2013-09-01" @default.
- W2089918128 modified "2023-10-17" @default.
- W2089918128 title "Efficient gradient descent algorithm for sparse models with application in learning-to-rank" @default.
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- W2089918128 doi "https://doi.org/10.1016/j.knosys.2013.06.001" @default.
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