Abstract
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.