Query Fine Tuning and Search Results Reranking Using Content Measure and Context Reference Algorithm

摘要

In this study, we propose a method to improve the precision of top N retrieved documents retrieved from the web by re-ordering the retrieved documents from a search engine. The user query is accepted and the search process is initiated by employing an external search engine. On the retrieved search results, content analysis is carried out and various measures of relevance are calculated. Based on the overall relevance measure, the search results are reranked. The search context plays a vital role in framing of the query and search process. Hence we propose an algorithm to perform the context analysis on the reranked results. The benefit of this is two fold. First, the user is given a preview about on what context the keywords are used in a document thus reducing the irrelevant document browsing time. Second, by viewing the context, the user can fine tune the search query to get a closer search result. From the experimental results we have found that the reranking based on our relevance measure shows improvement in the search result obtained from search results.