From 97b0adf617ece09b0612e18aa1c665a9f752dcfc Mon Sep 17 00:00:00 2001 From: dong zhihong Date: Mon, 15 May 2017 21:03:43 +0800 Subject: [PATCH] "format image " --- ltr/README.md | 38 +++++++++------------- ltr/image/hashring.png | Bin 0 -> 70732 bytes ltr/{ => image}/lambdarank.jpg | Bin ltr/{ => image}/learningToRank.jpg | Bin ltr/{ => image}/ranknet.jpg | Bin ltr/{ => image}/search-engine-example.png | Bin 6 files changed, 15 insertions(+), 23 deletions(-) create mode 100644 ltr/image/hashring.png rename ltr/{ => image}/lambdarank.jpg (100%) rename ltr/{ => image}/learningToRank.jpg (100%) rename ltr/{ => image}/ranknet.jpg (100%) rename ltr/{ => image}/search-engine-example.png (100%) diff --git a/ltr/README.md b/ltr/README.md index 8d78c220..bd2122f4 100644 --- a/ltr/README.md +++ b/ltr/README.md @@ -11,10 +11,9 @@ 排序学习技术随着互联网的快速增长而受到越来越多关注,是机器学习中的常见任务。一方面人工排序规则不能处理海量规模的候选数据,另一方面无法为不同渠道的候选数据给于合适的权重,因此排序学习在日常生活中应用非常广泛。排序学习起源于信息检索领域,目前仍然是许多信息检索场景中的核心模块,例如搜索引擎搜索结果排序,推荐系统候选集排序,在线广告排序等等。在本例子中,采用文档检索阐述排序学习模型。

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图1 排序模型在文档检索的典型应用搜索引擎中的作用

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+图1. 排序模型在文档检索的典型应用搜索引擎中的作用 +

假定有一组文档S,文档检索任务是依据和请求的相关性,给出文档排列顺序。查询引擎根据查询请求,排序模型会给每个文档打出分数,依据打分情况倒序排列文档,得到查询结果。在训练模型时,给定一条查询,并给出对应的文档最佳排序和得分。在预测时候,给出查询请求,排序模型生成文档排序。传统的排序学习方法划分为以下三类: @@ -30,14 +29,13 @@ Listwise方法是直接优化排序列表,输入为单条样本为一个文档排列。通过构造合适的度量函数衡量当前文档排序和最优排序差值,优化度量函数得到排序模型。由于度量函数很多具有非连续性,优化困难。 -

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图2 排序模型三类构造方法

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+图2. 排序模型构造的三类方法 +

+ ## 实验数据 本例子中的实验数据采用了排序学习中的基准数据[LETOR]([http://research.microsoft.com/en-us/um/beijing/projects/letor/LETOR4.0/Data/MQ2007.rar](http://research.microsoft.com/en-us/um/beijing/projects/letor/LETOR4.0/Data/MQ2007.rar))中语料库,部分来自于Gov2网站的查询请求结果,包含了约1700条查询请求结果文档列表,并对文档相关性做出了人工标注。其中,一条查询含有唯一的查询id,对应于多个具有相关性的文档,构成了一次查询请求结果文档列表。每个文档由一个一维数组的特征向量表示,并对应一个人工标注与查询的相关性分数。 @@ -50,7 +48,7 @@ score : query id : feature1, feature2, …., featureN. `mq2007`数据集分别提供了三种类型排序模型的生成格式,需要指定生成格式`format` -例如 +例如调用接口 ```python pairwise_train_dataset = functools.partial(paddle.dataset.mq2007.train, format="pairwise") @@ -60,8 +58,6 @@ for label, left_doc, right_doc in pairwise_train_dataset(): - - ## 模型概览 对于排序模型,本样例中提供了PairWise方法的模型RankNet和ListWise方法的模型LambdaRank,分别代表了两类学习方法。PointWise方法的排序模型退化为回归问题,不予赘述。 @@ -101,12 +97,9 @@ $$C=\frac{1}{2}(1-S_{i,j})\sigma (s_{i}-s{j})+log(1+e^{-\sigma (s_{i}-s_{j})})$$ 根据以上推论构造RankNet网络结构,由若干层隐藏层和全连接层构成,如图所示,将文档特征使用隐藏层,全连接层逐层变换,完成了底层特征空间到高层特征空间的变换。其中docA和docB结构对称,分别输入到最终的RankCost层中。

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图3 RankNet网络结构示意图

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+图3. RankNet网络结构示意图 +

- 全连接层(fully connected layer) : 指上一层中的每个节点都连接到下层网络。本例子中同样使用paddle.layer.fc实现,注意输入到RankCost层的全连接层输出为1x1的层结构 - RankCost层: RankCost层是排序网络RankNet的核心,度量docA相关性是否比docB好,给出预测值并和label比较。使用了交叉熵(cross enctropy)作为度量损失函数,使用梯度下降方法进行优化。细节可见[RankNet](http://icml.cc/2015/wp-content/uploads/2015/06/icml_ranking.pdf)[4] @@ -227,10 +220,9 @@ $$\lambda _{i,j}=\frac{\partial C}{\partial s_{i}}=-\frac{\sigma }{1+e^{\sigma ( 由以上推导可知,LambdaRank网络结构和RankNet结构非常相似。如图所示

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图4. LambdaRank的网络结构示意图

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+图4. LambdaRank的网络结构示意图 +

一个查询得到的结果文档列表作为一条样本输入到网络中,替换RankCost为LambdaCost层,其他结构与RankNet相同。 diff --git a/ltr/image/hashring.png b/ltr/image/hashring.png new file mode 100644 index 0000000000000000000000000000000000000000..1879aabda5beba02dc7b25475516f21aaac4b21b GIT binary patch literal 70732 zcmeEu^ ztD=MmOwl0G4tRlREvfMp1_qb<=?7Lyk?I%*MhHeqR9M*&c0UE-hqB6T_sn9JRwm)j zGelTnDn@jSR-u!ld;>DNn^~P={pQznWL3<;!T~Yg92^`N=1v|DNw{^5jXxiq&nJEs zaE<#q#DDT*De-Dyiw`3*B}GR_*V~hf3<`z&pI;N`ffY$D6vhAjE?I<+C%T3c>Hqp5 z_<%YlEO}YnJST=GIywXbD=hTyUx5V;&;Qp;@Ge6T__~Z`GL-RuUyF=PRCN*le=hyc zCF4Ye0t=|C-}~bJ?-9|_Dd>;>?*;z3jtm8v=w#H~D-q=Xd-qR6M8;nJKhH#sflgue z#{w%I;lGCP?df&-&n^G!&Re3u*DKenf`QkzCD_@bE04V=c)f0 zg1qdMdYM$-RXcoOno^q}2hGLfVwJ?67 z2>2RYm=Gkq6BbVd^6zDYa=~p4rp(#xm<^@!ON0>#PY_toXyg3UZ}koy5iwN}*6Zr- zuIEXrzyp{3DJB$XK5Q8Zef7^i|A5}p-#=e%wUiZOH+8sB{~?ucC*+!fl5!=ip#*6d z;Z<+VmB^Y)spFr=@mrC;kV#zozI^+G*}q@){_aj-g&^peH-4vuUcrLra);6Q{m(6t zML?}@n!%?y%qEZHr#1(YR{{?aePPXB*5i#w)J0Lth&*>PsmepbudREF*U0roRz~I> 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