Cluster-Based Adaptive Information Retrieval System

基于集群的自适应信息检索系统

• 批准号: 8805875
• 负责人: Vijay Raghavan
• 金额: $ 14.25万
• 依托单位: University of Louisiana at Lafayette
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 1988
• 资助国家: 美国
• 起止时间: 1988-07-01 至 1991-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=8805875&HistoricalAwards=false
• 关键词: Cluster; Based; Adaptive; Information; Retrieval

A retrieval system model that leads to an integrated approach in which both local and global feedback naturally blend into a unified process is proposed. The novelty of this approach is that the relevance feedback of a particular query instance as well as the accumulated knolwdge from past queries are directly related to the performance of the present query. This model represents a cluster-based approach to information retrieval. The cluster-based approach is developed around a clustering technique that captures the users concept of closeness between documents. Since the clustering technique to support the retrieval process is developed. The principal advantage of the proposed model is that the retrieval performance can be directly influenced by the optimization criterion employed during clustering. The significance of our approach lies in the fact that document representation and the measure of similarity among doucments do not have to be prespecified in an ad hoc manner. Instead such design alternatives can be dictated by cluster-scope of application than just information retrieval.

一个检索系统模型，导致一个集成的方法， 这两个本地和全球的反馈自然融合成一个 提出了统一的流程。 这种方法的新奇在于 特定查询实例的相关性反馈 以及从过去的查询中积累的知识直接 与当前查询的性能相关。 该模型 表示一种基于聚类的信息检索方法。 基于聚类的方法是围绕聚类开发的， 一种技术，它捕捉了用户对 证件 由于支持 开发了检索过程。 的主要优点 提出的模型是，检索性能可以直接 受优化标准的影响， 聚类 我们方法的重要性在于 文档表示和相似性度量 不需要以特别的方式预先指定参数。 相反，这样的设计替代方案可以由 集群的应用范围不仅仅是信息检索。