Cluster-Based Adaptive Information Retrieval System

提出了一种检索系统模型，该模型导致了一种综合的方法，其中局部和全局反馈自然地融合到一个统一的过程中。这种方法的新颖之处在于，特定查询实例的相关反馈以及从过去查询中积累的知识直接与当前查询的性能相关。该模型代表了一种基于集群的信息检索方法。基于聚类的方法是围绕一种聚类技术开发的，该技术捕获了用户对文档之间接近程度的概念。因为开发了支持检索过程的集群技术。该模型的主要优点是在聚类过程中采用的优化准则可以直接影响检索性能。我们方法的意义在于这样一个事实，即文档表示和面团之间的相似性度量不必以特别的方式预先指定。相反，这种设计替代方案可以由应用程序的集群范围决定，而不仅仅是信息检索。

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.