A PSO Strategy of Finding Relevant Web Documents using a New Similarity Measure
This addresses the problem of efficient and effective web information retrieval for users, but it appears incremental as it builds on existing methods with a new similarity measure and optimization.
The paper tackled improving web document retrieval by proposing a new similarity measure (SMDR) and using PSO to optimize the process, achieving better precision-recall rates than existing methods on CACM collections.
In the world of the Internet and World Wide Web, which offers a tremendous amount of information, an increasing emphasis is being given to searching services and functionality. Currently, a majority of web portals offer their searching utilities, be it better or worse. These can search for the content within the sites, mainly text the textual content of documents. In this paper a novel similarity measure called SMDR (Similarity Measure for Documents Retrieval) is proposed to help retrieve more similar documents from the repository thus contributing considerably to the effectiveness of Web Information Retrieval (WIR) process. Bio-inspired PSO methodology is used with the intent to reduce the response time of the system and optimizes WIR process, hence contributes to the efficiency of the system. This paper also demonstrates a comparative study of the proposed system with the existing method in terms of accuracy, sensitivity, F-measure and specificity. Finally, extensive experiments are conducted on CACM collections. Better precision-recall rates are achieved than the existing system. Experimental results demonstrate the effectiveness and efficiency of the proposed system.