III: Small: Modeling and Inferring Searcher Intent by Mining User Interactions

推断用户意图是信息检索和网络搜索中的一个核心问题：为了有效地排名和结果呈现，搜索引擎必须知道用户在寻找什么。然而，表达一个不确定的信息需求目前依赖于输入？是吧？搜索关键字，这可能需要多轮的试错从搜索引擎。本计画的目标是开发有效的方法，让搜寻引擎能自动地从网路互动与行为资料中推断网路意图与资讯需求。具体来说，该项目解决了搜索意图推断的两个主要挑战：开发准确和强大的搜索意图和行为模型，并利用这些模型来推断每个用户的搜索意图。该项目通过考虑广泛的用户交互和上下文特征，并通过开发用于挖掘和利用这些信号的新技术来改善网络搜索和信息访问，显着推进了先前在隐式反馈和搜索建模方面的努力。该项目使用机器学习和数据挖掘技术来模拟搜索行为和结果页面行为之间的联系以及搜索意图。该项目的第一阶段通过结合眼动追踪和搜索界面仪器数据，在受控的实验室环境中开发和评估这些模型。该项目的第二阶段经验验证的意图推理模型，通过大规模收集的搜索行为数据，使用各种远程用户研究仪表搜索界面。最后，该项目应用所产生的模型和算法，以提高性能的关键信息检索任务，包括结果排名，自动查询扩展，搜索结果的呈现。通过与主要搜索引擎公司的合作，该项目开发的技术预计将使数百万用户的网络搜索和信息访问更加直观和有效。通过在特定领域应用所开发的技术，从改进图书馆搜索到基于网络的认知障碍诊断，将产生更广泛的影响。该项目的所有方面都将涉及研究生和本科生，由此产生的工具和数据集将被整合到本科课程教学和项目中，从而扩大对计算机科学研究的参与。由此产生的出版物、软件和数据集将在项目网站（http：//ir.mathcs.emory.edu/intent/）上公布。

Inferring searcher intent is a central problem in information retrieval and web search: for effective ranking and result presentation, the search engine must know what the user is looking for. Yet, expressing a searcher information need currently relies on entering the ?right? search keywords, which can require multiple rounds of trial-and-error from the searcher. The goal of this project is to develop effective methods for a search engine to automatically infer searcher intent and information needs from the searcher interactions and behavior data. Specifically, the project addresses two main challenges of search intent inference: developing accurate and robust models of searcher intent and behavior, and exploiting these models to infer search intent for each individual user. This project significantly advances previous efforts on implicit feedback and search modeling, by considering a wide range of user interaction and contextual features, and by developing novel techniques for mining and exploiting these signals to improve web search and information access.To develop robust search intent and behavior models, the project uses machine learning and data mining techniques to model the connection between search actions and result page behavior and the searcher intent. The first stage of the project develops and evaluates these models in controlled lab environments, by combining eye tracking and search interface instrumentation data. The second stage of the project empirically validates the intent inference models through a large-scale collection of search behavior data using a variety of remote user studies with instrumented search interfaces. Finally, the project applies the resulting models and algorithms to improve performance on key information retrieval tasks including result ranking, automatic query expansion, and search result presentation. The techniques developed in this project are expected to make web search and information access more intuitive and effective for millions of users through collaboration with major search engine companies. Additional broader impacts will be achieved through domain-specific applications of the developed techniques, ranging from improved library search to web-based diagnostics of cognitive impairment. All aspects of the project will involve graduate and undergraduate students, and the resulting tools and datasets are to be integrated into undergraduate course instruction and projects, thus broadening participation in computer science research. The resulting publications, software, and datasets will be made publicly available on the project website (http://ir.mathcs.emory.edu/intent/).