Extreme Learning to Rank via Low Rank Assumption论文解读
2020-05-07 04:08
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在推荐系统和网页搜索中为数十万的用户执行ranking是很常见的。学习单一的ranking function不可能捕捉所有用户的易变性,然而为每个用户学习一个ranking function 是很耗时的,同时也需要来自每个用户的大量数据。
为了解决这个问题,本文作者提出了Factorization RankSVM算法,该算法通过学习k个基础的函数,然后为将这k个ranking function进行线性组合,使得每一个用户有一个ranking function.通过利用low-rank结构,开发了一个更快的算法去减少梯度下降的时间复杂度。同时也证明了他们所提出的方法的泛化误差要好于为每个用户单独执行RankSVN训练一个ranking function.
Introduction
LTR Pairwise method
给你一个具有x1,x2,...xnx_1,x_2,...x_n
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