A Fast Algorithm for Learning a Ranking Function from Large-Scale Data Sets

最新推荐文章于 2023-01-09 17:33:47 发布
原创 最新推荐文章于 2023-01-09 17:33:47 发布 · 137 阅读 · 0 ·
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A Fast Algorithm for Learning a Ranking Function from Large-Scale Data Sets

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Learning to ranking简介 目录 1前言... 1 2 LTR基本流程... 2 3 模型训练... 3 3.1排序类型... 3 3.1.1 Pointwise. 3 3.1.2 Pairwise. 4 3.1.3 Listwise. 5 3.2 排序算法分类... 6 4评价指标... 6 4.1 NDCG.. 7 4.2 MAP.
机器学习排序之Learning to Rank简单介绍
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[论文阅读笔记]2020_WWW_Few-Shot Learning for New User Recommendation in Location-based Social Networks 论文下载地址： https://doi.org/10.1145/3366423.3379994 发表期刊：WWW Publish time: 2020 作者及单位: Ruirui Li UCLA rrli@cs.ucla.edu Xian Wu Univeristy of Notre Dame xwu9@nd.ed
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Directly optimization of evaluation measure in information retrieval
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 文章首先针对adarank, svm-map提出它们的共同点:均是优化IR measure的上界, 但是前者的界 是基于IR measure,     后者的界是基于最优排序和非最优排序的对,形式为: 仔细观察发现,将PERMU-rank写成类似(9)的形式,和SVMMAP的区别仅仅是把max变成了求和!  这篇文章太扯蛋了...  permurank利用了第二种bound的构造形式...
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Hinton首次提出深度学习的概念是在Reduc the dimensionality of data with neural networks还是 a fast learning algorithm for deep belief nets中？
07-16
对不起，我之前的回答有误。Hinton首次提出深度学习的概念是在2006年的论文《Reducing the dimensionality of data with neural networks》中，而非《A fast learning algorithm for deep belief nets》。在这篇论文中，Hinton讨论了使用深度神经网络进行数据降维的方法，这被认为是深度学习的前身之一。感谢您的纠正。
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