Optimal Page Ranking Technique for Webpage Personalization Using Semantic Classifier

Personalized webpage ranking is one of the key components in search engines. Moreover, most of the existing search engines focus only on answering user queries, although personalization will be more and more important as the amount of information available on the Web increases. Even though various re-ranking algorithms are developed, providing prompt responses to the user query results in a major challenge in web page personalization. Therefore, an efficient and effective ranking algorithm named the Oppositional Grass Bee optimization algorithm is developed to re-rank the web documents in the webpage personalization system. The proposed algorithm is designed by integrating the Oppositional Grass Hopper (OGHO) and Artificial Bee Colony optimization (ABC) algorithms. The concept of fictional computing and the foraging behavior realize the re-ranking process more effectively in the web environment. However, the semantic features extracted from the web pages make the process more effective and achieve optimal global solutions through the fitness measure. The proposed OGBEE Ranking algorithm effectively captures and analyzes the ranking scores of different search engines in order to generate the reranked score result.

Keywords: Page ranking, Artificial bee colony optimization, Web mining, Grass hopper optimization, Feature Extraction, Web Personalization, Factor based Features, Rank-based features, Hyperlink-based ranking, Hyperlink induced topic search, ENN-based Opposition Behavior Learning.