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应用程序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，例如地理位置、季节、时间和信息。用户的搜索活动变得更加复杂，并且通常基于任务，生成各种反馈和参与信号，例如点击、鼠标移动、眼球追踪结果和查询重新表述。此外，搜索不仅仅是单个用户的个性化活动。以及许多具有类似行为的用户共享的活动信息需求 搜索引擎面临着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，这要求搜索引擎从基本上寻找单个查询的文档升级为决策。通过在论文和工具中传播研究成果，该项目将产生三种广泛的影响：首先，该项目开发的技术将惠及广大日常用​​户并赋予他们权力。处理复杂的、面向任务的网络搜索。其次，开发的算法和软件将为其他研究人员和从业者提供一些有用的工具，用于解决结合动力学的 IR 问题。然而，要开始使用拟议的新搜索引擎，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的绝佳项目。这些 STEM 学科的少数群体。该项目旨在培养下一代搜索引擎，更具体地说，是决策引擎。重点是设计、实验和部署用于对搜索过程中呈现的动态进行建模的统计模型。（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环境中的正确位置，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；用户对信息空间的探索优化搜索引擎的动作和算法；（4）对用户和搜索引擎之间以及多个用户之间的交互进行建模，为他们之间的互动和博弈创造动态环境，实现双赢。该项目的成功将开启 IR 的一个新的研究领域：动态 IR 建模，这项研究的结果将对下一代搜索引擎产生巨大的影响。 IR 中的强化学习和博弈论领域红外。