Directly optimization of evaluation measure in information retrieval

最新推荐文章于 2024-11-07 23:58:36 发布
最新推荐文章于 2024-11-07 23:58:36 发布
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分类专栏： 机器学习 文章标签： 算法 框架 C C++ C#
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本文链接：https://blog.youkuaiyun.com/iteye_12567/article/details/81923075
本文探讨Adarank与SVM-MAP两种信息检索算法的相似之处及不同点，指出两者都旨在优化IR measure的上限，但采用的方法不同。Adarank基于IR measure，而SVM-MAP则基于最优排序与非最优排序对。文中还讨论了PERMURank的设计思路，即模仿SVM-MAP损失函数的构造方式。


 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,说白了就是仿照SVM-MAP的loss来构造,构造出对第i个查询的loss形式为

对它进行优化.该形式中的B_i和C_i是最大违反约束集,实质是大PI_i*和大PI_i的子集.

 

 

这篇文章看后,有这么几点值得我借鉴的:

1, 从多篇类似的文章中,找到共通的框架, 利用这个框架,设计出不同的算法;

2, 在设计不同算法时,主要是从构造loss的方面考虑,其次是从使用的机器学习技术考虑(目前对我来说,哪种技术的实现源代码易用,benchmark效果好选哪种)

 

另外,svm-map的缺点是只针对MAP这种二元分类的IR-measure可行,因为在求delta-map时用到了这个observation.

 

 

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