CAREER: Statistical Information Retrieval Modeling for Complex Search

职业：复杂搜索的统计信息检索建模

• 批准号: 1453721
• 负责人: Grace Hui Yang
• 金额: $ 55.2万
• 依托单位: Georgetown University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-02-01 至 2021-01-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1453721&HistoricalAwards=false
• 关键词: CAREER; Statistical; Information; Retrieval; Modeling

With the increasing popularity of Web applications and users' deep involvement in the Web, search engines face great challenges with a new degree of complexity. For instance, location-based services collect more complex contextual information such as geo-locations, season, time and temperature. Users' search activities have become more complex and usually task-based generating a variety of feedback and engagement signals such as clicks, mouse movements, eye tracking results, and query reformulations.  Moreover, search is not only an individual user's personalized activity, but also activities shared by many users with similar information needs. Search engines are presented with the richest types of information and the largest amount of data ever and the complexity of the available information is tremendous. This demands that search engines be upgraded from retrieval systems that basically look for documents for single queries to decision engines that can pick the best choices for information seeking tasks. Through disseminating research results in papers and tools, the project will make three types of broad impact. First, the techniques developed in this project will benefit a broad population of everyday users and empower them to deal with complex, task-oriented web search. Second, the algorithms and software developed will provide fellow researchers and practitioners a handful of useful tools for solving IR problems incorporating dynamics. Third, the project will reach out to middle school girls and elementary school students. It will be easy for any search engine user to start using the proposed new search engine. However, to be an expert on IR, students need to be good at mathematics, natural language processing, user interface, artificial intelligence, and programming. This will be an excellent project to attract young people and minorities to these STEM disciplines.This project aims to create the next generation search engines, to be more specific, decision engines. The focus will be on designing, experimenting, and deploying statistical models for modeling the dynamics presented in the search process. The technical challenges are: (1) given the complexity of the available data, integrating a search engine appropriately into the right places in the larger context for the ultimate information seeking tasks; (2) providing theoretical and practical support to formal modeling of user engagement and other dynamics in retrieval models for better retrieval effectiveness; (3) modeling a user's exploration in the information space and optimizing a search engine's actions and algorithms; and (4) modeling interactions between a user and a search engine as well as interactions among multiple users, creating the dynamic environment for them all to interact and to game with each other and achieve a win-win optimization. The success of this project will start a new research field in IR: dynamic IR modeling. The results of this research will be highly influential with great impact on the next generation search engines. The work will build a foundation for future advances in the fields of reinforcement learning in IR and game theory in IR.

随着Web应用的日益普及和用户对Web的深入参与，搜索引擎面临着新的复杂程度的巨大挑战。例如，基于位置的服务收集更复杂的上下文信息，如地理位置，季节，时间和温度。用户的搜索活动变得越来越复杂，通常是基于任务的，会产生各种反馈和参与信号，如点击、鼠标移动、眼动跟踪结果和查询重构。此外，搜索不仅是单个用户的个性化活动，也是许多具有相似信息需求的用户共享的活动。搜索引擎呈现出有史以来最丰富的信息类型和最大的数据量，并且可用信息的复杂性是巨大的。这就要求搜索引擎从基本上为单个查询查找文档的检索系统升级到可以为信息搜索任务选择最佳选择的决策引擎。通过在论文和工具中传播研究成果，该项目将产生三类广泛影响。首先，这个项目开发的技术将使广大的日常用户受益，使他们能够处理复杂的、面向任务的网络搜索。其次，算法和软件开发将提供同行研究人员和从业人员解决IR问题纳入动态的一些有用的工具。第三，该项目将覆盖中学女生和小学生。任何搜索引擎用户都可以很容易地开始使用拟议中的新搜索引擎。然而，要成为IR专家，学生需要擅长数学，自然语言处理，用户界面，人工智能和编程。这将是一个吸引年轻人和少数族裔学习STEM学科的优秀项目。该项目旨在创建下一代搜索引擎，更具体地说，是决策引擎。重点将是设计，实验和部署统计模型，用于模拟搜索过程中呈现的动态。技术挑战是：（1）考虑到可用数据的复杂性，将搜索引擎适当地集成到更大背景中的正确位置，以实现最终的信息搜索任务;（2）为检索模型中的用户参与和其他动态的正式建模提供理论和实践支持，以提高检索效率;（3）对用户在信息空间中的探索进行建模，并优化搜索引擎的动作和算法;对用户与搜索引擎之间的交互以及多个用户之间的交互进行建模，创造一个动态的环境，让他们互相影响，互相博弈，达到双赢的最优化。该项目的成功将开创一个新的研究领域：动态红外建模。本文的研究成果将对下一代搜索引擎产生重大影响。这项工作将为IR中的强化学习和IR中的博弈论领域的未来发展奠定基础。