摘要翻译：
网页排序和协同过滤需要优化复杂的性能度量。目前的支持向量方法不能直接优化它们，而是侧重于两两比较。我们提出了一种新的方法，它允许直接优化相关的损失函数。这是通过Hilbert空间中的结构化估计来实现的。与最大边际马尔可夫网络优化多变量性能指标关系最密切。该方法的关键在于，在训练过程中，排序问题可以看作是一个线性分配问题，可以用匈牙利婚姻算法求解。在测试时，一个排序操作就足够了，因为我们的算法为每个（文档、查询）对分配一个相关性分数。实验表明，该算法速度快，效果良好。
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英文标题：
《Direct Optimization of Ranking Measures》
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作者：
Quoc Le and Alexander Smola
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最新提交年份：
2007
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分类信息：

一级分类：Computer Science 计算机科学
二级分类：Information Retrieval 信息检索
分类描述：Covers indexing, dictionaries, retrieval, content and analysis. Roughly includes material in ACM Subject Classes H.3.0, H.3.1, H.3.2, H.3.3, and H.3.4.
涵盖索引，字典，检索，内容和分析。大致包括ACM主题课程H.3.0、H.3.1、H.3.2、H.3.3和H.3.4中的材料。
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一级分类：Computer Science 计算机科学
二级分类：Artificial Intelligence 人工智能
分类描述：Covers all areas of AI except Vision, Robotics, Machine Learning, Multiagent Systems, and Computation and Language (Natural Language Processing), which have separate subject areas. In particular, includes Expert Systems, Theorem Proving (although this may overlap with Logic in Computer Science), Knowledge Representation, Planning, and Uncertainty in AI. Roughly includes material in ACM Subject Classes I.2.0, I.2.1, I.2.3, I.2.4, I.2.8, and I.2.11.
涵盖了人工智能的所有领域，除了视觉、机器人、机器学习、多智能体系统以及计算和语言（自然语言处理），这些领域有独立的学科领域。特别地，包括专家系统，定理证明（尽管这可能与计算机科学中的逻辑重叠），知识表示，规划，和人工智能中的不确定性。大致包括ACM学科类I.2.0、I.2.1、I.2.3、I.2.4、I.2.8和I.2.11中的材料。
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英文摘要：
Web page ranking and collaborative filtering require the optimization of sophisticated performance measures. Current Support Vector approaches are unable to optimize them directly and focus on pairwise comparisons instead. We present a new approach which allows direct optimization of the relevant loss functions. This is achieved via structured estimation in Hilbert spaces. It is most related to Max-Margin-Markov networks optimization of multivariate performance measures. Key to our approach is that during training the ranking problem can be viewed as a linear assignment problem, which can be solved by the Hungarian Marriage algorithm. At test time, a sort operation is sufficient, as our algorithm assigns a relevance score to every (document, query) pair. Experiments show that the our algorithm is fast and that it works very well.
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