基于概率的文档和用户属性聚类
Probability Based Clustering for Document and User Properties
摘要：
信息检索系统可以通过利用诸如用户和文档特征的上下文信息来改进。本文提出了一个模型的基础上重叠的概率或模糊聚类等功能。该模型被应用在一个融合方法，线性结合几个检索系统。融合是基于权重的不同的检索系统，这是通过利用相关反馈信息学习。可以通过为每个文档和用户集群维护一个模型来改进此计算。这样，就可以确定和应用每种文件或用户类型的最佳检索系统。本文中介绍的扩展允许重叠的、概率性的特征聚类，以进一步细化该过程。
英文摘要：
Information Retrieval systems can be improved by exploiting context information such as user and document features. This article presents a model based on overlapping probabilistic or fuzzy clusters for such features. The model is applied within a fusion method which linearly combines several retrieval systems. The fusion is based on weights for the different retrieval systems which are learned by exploiting relevance feedback information. This calculation can be improved by maintaining a model for each document and user cluster. That way, the optimal retrieval system for each document or user type can be identified and applied. The extension presented in this article allows overlapping, probabilistic clusters of features to further refine the process.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
