Automatic Annotating Search Results with Relevance Feedback for User Search Goals

Abstract

Information retrieved form web database which contain data in html format. For more understanding of user need to extract the html pages and assign labels mean Data Alignment is need for Data units for html documents . Then, for each group annotate it from different aspects and aggregate the different annotations to predict a final annotation label for it. An annotation wrapper for the search site is automatically constructed and can be used to annotate new result pages from the same web database. Users search with accuracy and speed goals is to study law. This method limits the conditions suffered in the search accuracy and speed. Currently the main aim for more improvements and approaches to Web user satisfaction of search is the basis for the goals. Users search for goals different methods literature review to present the new framework and proposed methods and insightful analysis algorithms and evaluate its performance. First, we propose framework automatic annotation for retrieved documents by clustering the same contain documents and assign data units for each cluster . Feedback sessions are constructed from user click-through logs and can efficiently reflect the information needs of users. Finally, we propose a new criterion “Classified Average Precision (CAP)” to evaluate the performance of inferring user search goals. Experimental results are presented using user click-through logs from a commercial search engine to validate the effectiveness of our proposed methods.
DOI: 10.17762/ijritcc2321-8169.150768