III: Small: Models and Measures for Novel and Diverse Search Results

传统的信息检索模型假定文档是独立相关的。但是，用户通常希望看到检索到的文档涵盖几个子主题，或者不同的可能解释，或者查询的相关信息片段。在这些情况下，将文档建模为独立相关并不能提供最佳的用户体验。本研究项目试图用新的文件相互依赖模型和新的评价措施来补救这一问题。这项工作有三个主线：(1)实现排名算法所需的多样性、新颖性和冗余模型；(2)衡量文件排序中的多样性、新颖性和冗余性；(3)对模型结构和参数进行优化。该项目解决的主要挑战包括：如何对不同任务和领域的搜索结果之间的相关性以及相互依赖性进行建模；如何预测多样性程度；以及如何评估新模型。新的检索模型包括基于集合的检索模型和基于模式的检索模型，新的度量方法包括基于集合的标注、基于列表的偏好等。模型将用现有的方法和新方法进行评估，新方法将通过深入的统计分析进行评估。该项目的结果包括一套将新颖性和多样性与不同领域的相关性相结合的模型和措施，包括生物医学搜索、法律搜索和网络搜索。这个项目的深远的潜在影响是全面提高搜索引擎的效用。该项目涉及生物医学搜索、法律或专利搜索的具体应用，而网络搜索反映了搜索引擎重要的主要领域，在这些领域，改进的新颖性和多样性模型将直接改善用户体验。项目结果，包括出版物和评价数据集，将通过项目网站（http://ir.cis.udel.edu/monads）发布。

Traditional models of information retrieval assume documents are independently relevant. However, users often want to see the retrieved documents cover several subtopics, or different possible interpretations, or nuggets of relevant information for a query. In these cases, modeling documents as independently relevant does not provide the optimal user experience. This research project attempts to remedy this with new models of document interdependence and new evaluation measures. There are three threads running through this work: (1) the models of diversity, novelty, and redundancy that will be needed to implement ranking algorithms; (2) measurements of diversity, novelty, and redundancy in a ranking of documents; and (3) optimizing model structures and parameters to the measures.The main challenges addressed in this project include: how to model relevance along with interdependencies among search results for different tasks and domains; how to predict the degree of diversity; and how to evaluate the new models. The new models include set-based and pattern-based retrieval models, and the new measures include set-based labeling, list-based preference etc. The models will be evaluated with the existing measures as well as the new measures, and the new measures will be evaluated with in-depth statistical analysis. The result of this project includes a suite of models and measures that combine novelty and diversity with relevance for different domains, including biomedical search, legal search and Web search.The far-reaching potential impact of this project is an improvement in search engine utility across the board. The project addresses specific applications to biomedical search, legal or patent search, and web search reflect major domains in which search engines are important and in which improved models for novelty and diversity would directly improve the user experience. Project results, including publications and evaluation datasets, will be disseminated via the project website (http://ir.cis.udel.edu/monads).