学习通过相关性融合
Learning to Blend by Relevance
摘要：
各种垂直搜索引擎的出现凸显了一个事实，即单一的排名技术无法处理搜索问题的复杂性和规模。例如，视频和图像搜索背后的技术与一般的网络搜索非常不同。它们的排名功能几乎没有共同点。问答网站（例如，哇哦！答案）可以利用为一般web开发的文本匹配和点击功能，但它们有独特的页面结构和丰富的用户反馈，例如，大拇指向上和大拇指向下评级在雅虎！答案，这大大有利于自己的排名。即使对于答案和一般网络共享的那些特征，特征和相关性之间的相关性也可能非常不同。因此，需要专门的功能，以便更好地对各个域中的文件进行排名。这些专用函数定义在不同的特征空间上。然而，每个域都有一个搜索框，既不高效也不可扩展。而不是键入相同的查询两次到两个雅虎！搜索和Yahoo！回答和检索两个排名列表，我们宁愿把它只一次，但从这两个领域的主题收到一个全面的文件列表。这种情况需要新的技术，将来自不同来源的文档混合到一个单一的排名列表中。尽管混合列表的内容丰富，但它仍然必须按照相关性进行排序，我们称这种技术为混合，这是本文的主要主题。
英文摘要：
Emergence of various vertical search engines highlights the fact that a single ranking technology cannot deal with the complexity and scale of search problems. For example, technology behind video and image search is very different from general web search. Their ranking functions share few features. Question answering websites (e.g., Yahoo! Answer) can make use of text matching and click features developed for general web, but they have unique page structures and rich user feedback, e.g., thumbs up and thumbs down ratings in Yahoo! answer, which greatly benefit their own ranking. Even for those features shared by answer and general web, the correlation between features and relevance could be very different. Therefore, dedicated functions are needed in order to better rank documents within individual domains. These dedicated functions are defined on distinct feature spaces. However, having one search box for each domain, is neither efficient nor scalable. Rather than typing the same query two times into both Yahoo! Search and Yahoo! Answer and retrieving two ranking lists, we would prefer putting it only once but receiving a comprehensive list of documents from both domains on the subject. This situation calls for new technology that blends documents from different sources into a single ranking list. Despite the content richness of the blended list, it has to be sorted by relevance none the less. We call such technology blending, which is the main subject of this paper.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
