A Customized Content Optimization Using User Search Behavior Analysis

Search and content optimizations are very popular nowadays due to intensive growth of networks and data. There is a need to satisfy the user in the searching era. There are several techniques have been proposed to identify users search interest based on their searching history. The current proposal deals with the analysis of user search goals with the effective feedback. Exiting analytical process for individual interest mining from personalized weblog is a tedious process, because the existing techniques considered only the "click" based priority. The proposed system considers total number of clicks; unclicked URLs and time spend by the user in a particular page and links. Based on these parameters the personalization has been proposed. The Implementation of existing algorithm for web usage mining and analyzing the user feedback has a main drawback which is feedback collection issue. In this paper, three factors are analyzed which are personal interest, clicked and unclicked link similarity, and personal search sequence. The above factors are blended into a cohesive personalized search model and content optimization based on data mining techniques. This paper proposes an implicit and explicit model for analyzing the user search goals effectively. Research Scholar, Department of MCA RVS Arts and Science College, Sulur, priyadharsini 195@gmail.com Assistant Professor, Department of MCA RVS Arts and Science College, Sulur, kathirsujith@gmail.com.Index Terms-Feed Back sessions, Session clustering. Pseudo documents, LSC