Abstract: Content optimization has widely used in personalized search engines for better personalized results. It can be generated by the user actions and events on the web search engine. User interaction on the page plays a vital role in recommender systems.  Previous studies on recommender systems mainly focused on modeling techniques and feature development, this content optimization is based on general behavior analysis algorithm.  It provides user action analysis is critical for a recommender system.   The system proposes a novel implicit user feedback and event monitoring schemes for efficient content optimization. For this our system proposes PCO approach.  But user interactions in real-world Web applications are unlikely to be as ideal as those assumed by previously proposed models. Our proposed system builds an online dynamic learning framework for personalized recommendation. The main contribution in this paper is an approach of personalizing users' searches to achieve better search result which is based on event monitoring and personalized content optimized search.


Keywords: Content optimization, web search engine, Event monitoring, PCO and DKMC.