Improving Information Retrieval with Machine Learning

通过机器学习改进信息检索

• 批准号: 46392-2012
• 负责人: Ling, Charles
• 金额: $ 2.48万
• 依托单位: University of Western Ontario
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2018
• 资助国家: 加拿大
• 起止时间: 2018-01-01 至 2019-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=657687
• 关键词: Improving; Information; Retrieval; Machine; Learning

Information retrieval (IR) systems including various search engines have played important roles in our life. They include general web search engines (such as Google), vertical search engines (such as product search in Amazon.com), specific media search (such as image search), and so on. In the past years I have made fruitful and significant contributions in several important areas of machine learning. The objectives of this research program are to expand my research in machine learning, and leverage it to improve the performance and user experience of IR systems for people to easily obtain highly accurate and useful information.**One important question we will study in this research program is how hierarchies can be used to effectively improve search accuracy and user experience. Hierarchies or taxonomies on topics, genres, media types, and so on, are natural to human and important in organizing human knowledge. It would be ideal if keyword search and hierarchical browsing (roll-up and drill-down) can be seamlessly integrated. For example, searching "china" in the art category will remove ambiguity of china as the country. However, this has not been done in web search engines (such as Google), because billions of webpages must be classified into hierarchies by machine learning (i.e., not by human as in DMOZ, the Open Directory Project). Other fascinating research challenges include: how can active learning with generalized queries (our work) improve relevance feedback in IR? How can we utilize many "cheap" labellers (such as Amazon Mechanical Turk) in active learning of hierarchies? How can we learn from behaviours and cognitive models of users of IR systems? **The outcome and impact of our research program will be fruitful and significant. First of all, it will greatly advance both machine learning and IR research. We will continue to publish top-rated conference and journal papers. Second, we will build prototypes of advanced IR systems and search engines with machine learning techniques. Third, we will improve usability of our IR prototypes in large-scale user studies. Our ultimate objective of this research is to make it easy for people to obtain highly accurate and useful information.******

包括各种搜索引擎在内的信息检索（IR）系统在我们的生活中发挥了重要作用。它们包括通用网络搜索引擎（如Google）、垂直搜索引擎（如Amazon.com中的产品搜索）、特定媒体搜索（如图像搜索）等。在过去的几年里，我在机器学习的几个重要领域做出了卓有成效的重大贡献。该研究计划的目标是扩展我在机器学习方面的研究，并利用它来提高红外系统的性能和用户体验，以便人们轻松获得高度准确和有用的信息。**我们将在本研究中研究的一个重要问题程序是如何使用层次结构来有效提高搜索准确性和用户体验的。关于主题、流派、媒体类型等的层次结构或分类法对人类来说是很自然的，并且对于组织人类知识很重要。如果关键字搜索和分层浏览（上卷和下钻）能够无缝集成，那就太理想了。例如，在艺术类别中搜索“china”将消除“china”作为国家的歧义。然而，网络搜索引擎（例如 Google）尚未做到这一点，因为必须通过机器学习将数十亿网页分类为层次结构（即，而不是像 DMOZ（开放目录项目）那样由人类分类）。其他有趣的研究挑战包括：如何利用广义查询（我们的工作）进行主动学习来改善 IR 中的相关性反馈？我们如何利用许多“廉价”贴标机（例如 Amazon Mechanical Turk）来主动学习层次结构？我们如何从红外系统用户的行为和认知模型中学习？ **我们的研究计划的成果和影响将是卓有成效且意义重大的。首先，它将极大地推进机器学习和信息检索研究。我们将继续发表高评价的会议和期刊论文。其次，我们将利用机器学习技术构建先进的红外系统和搜索引擎的原型。第三，我们将提高 IR 原型在大规模用户研究中的可用性。我们这项研究的最终目标是让人们轻松获得高度准确且有用的信息。 *****