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Contextualized web search: Query-dependent ranking and social media search .

机译：内容相关的网络搜索：依赖查询的排名和社交媒体搜索。

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摘要

Due to the information explosion on the Internet, effective information search techniques are required to retrieve the desired information from the Web. With much analysis on users' search intention and the variant forms of Web content, we find that both the query and the indexed web content are often associated with various context information, which can provide much essential information to indicate the ranking relevance in Web search. Although there have been many existing studies on extracting the context information of both the query and the Web content, little research has addressed exploring these context information to improve Web search. This dissertation seeks to develop new search algorithms and techniques by taking advantage of rich context information to improve search quality.;This dissertation consists of two major parts. In the first one, we study how to explore the context information of the query to improve search performance. Since Web queries are usually very short, it is difficult to extract precise information need from the query itself. We propose to take advantage of the context information, such as the search intention of the query, to improve the ranking relevance. According to the query difference in terms of search intention, we first introduce the query-dependent loss function, by optimizing which we can obtain better ranking model. However, in practical search engine, it is uneasy to precisely define the query-dependent loss function. And, inspired by the requirement of deep dive and incremental update on dedicated ranking models, we investigate a divide-and-conquer framework for ranking specialization. Experimental results on a large scale data set from a commercial search engine demonstrate significant improvement on search performance over currently applied ranking models without considering query context.;The second part of this dissertation investigates how to extract the context of specific Web content and explore them to build more effective search system. This study focuses on searching over social media, the new emerging form of Web content. As the fastest growing segment of the Web, social media services establish new forums for content creation Daily, huge amount of social media content are collaboratively generated by millions of Web users, driven by various of social activities. Due to the valuable information contained in the resulting archives of both the content and the context of the interactions, computational methods for knowledge acquisition has become an important topic in social media analysis. Unlike traditional Web content, social media content is inherently associated with much new types of context information, including content quality, user reputation, and user interactions, all of which provide useful information for acquiring knowledge from social media. In this dissertation, we seek to develop algorithms and techniques for effective knowledge acquisition from collaborative social media environments by using the dynamic context information. In particular, this study first proposes a new general framework for searching social media content, which integrates both the content features and the user interactions. Then, a semi-supervised framework is proposed to explicitly compute content quality and user reputation in social media. These new context information are incorporated into the general search framework to improve the search quality. Experimental results of large scale evaluation on real world social media content demonstrate that this research achieves significant improvements over previous approaches for information search in social media. Furthermore, this dissertation also investigates techniques for extracting the structured semantics of social media content. Experimental results demonstrate that this kind of context information is essential for improving the performance of content organization and retrieval over social media service.
机译：由于Internet上的信息爆炸，需要有效的信息搜索技术才能从Web检索所需的信息。通过大量分析用户的搜索意图和Web内容的变体形式，我们发现查询和索引的Web内容通常都与各种上下文信息相关联，这可以提供很多必要的信息来指示Web搜索中的排名相关性。尽管已有许多关于提取查询和Web内容的上下文信息的研究，但很少有研究致力于探索这些上下文信息以改进Web搜索。本文旨在利用丰富的上下文信息来提高搜索质量，从而开发出新的搜索算法和技术。在第一个中，我们研究如何探索查询的上下文信息以提高搜索性能。由于Web查询通常很短，因此很难从查询本身中提取准确的信息需求。我们建议利用上下文信息（例如查询的搜索意图）来改善排名相关性。根据查询意图的查询差异，首先引入查询相关的损失函数，通过优化可以获得更好的排序模型。但是，在实际的搜索引擎中，要精确定义依赖于查询的损失函数并不容易。并且，出于对专门的排名模型进行深入研究和增量更新的要求的启发，我们研究了排名专业化的“分而治之”框架。来自商业搜索引擎的大规模数据集的实验结果表明，与当前应用的排名模型相比，在不考虑查询上下文的情况下，搜索性能有了显着提高。;本论文的第二部分探讨了如何提取特定Web内容的上下文并对其进行探索。建立更有效的搜索系统。这项研究的重点是搜索社交媒体，这是Web内容的新兴形式。作为Web上增长最快的部分，社交媒体服务每天为内容创建建立新的论坛，在各种社交活动的推动下，数百万的Web用户协作生成了大量社交媒体内容。由于在交互的内容和上下文的结果归档中都包含有价值的信息，因此知识获取的计算方法已成为社交媒体分析中的重要主题。与传统的Web内容不同，社交媒体内容固有地与许多新型的上下文信息相关联，包括内容质量，用户信誉和用户交互，所有这些都为从社交媒体获取知识提供了有用的信息。本文力求开发利用动态上下文信息从协作社交媒体环境中有效获取知识的算法和技术。特别是，本研究首先提出了一种用于搜索社交媒体内容的新通用框架，该框架将内容功能和用户交互集成在一起。然后，提出了一种半监督框架来显式计算社交媒体中的内容质量和用户声誉。这些新的上下文信息被合并到常规搜索框架中以提高搜索质量。对现实世界中社交媒体内容进行大规模评估的实验结果表明，该研究与以前在社交媒体中进行信息搜索的方法相比，取得了显着改进。此外，本文还研究了提取社交媒体内容的结构化语义的技术。实验结果表明，这种上下文信息对于提高内容组织和社交媒体服务检索的性能至关重要。

著录项

  • 作者

    Bian, Jiang.;

  • 作者单位

    Georgia Institute of Technology.;

  • 授予单位 Georgia Institute of Technology.;
  • 学科 Computer Science.
  • 学位 Ph.D.
  • 年度 2010
  • 页码 193 p.
  • 总页数 193
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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