WeWeb Page Re-Ranking using Squirrel Search Rank Algorithm

Abstract

Ranking of web page is one of the key factors in search engine. However, ranking is intended to offer the list of ranked web documents that user is more likely to desire, but retrieving the relevant documents at faster rate based on the user query poses a great challenge in web environments. Hence, an effective web page re-ranking mechanism named Squirrel Search Rank algorithm (SqSRank) is developed in this research for re-ranking web pages more effectively with re-ranking measure. However, the proposed SqSRank algorithm is designed using the Squirrel Search Algorithm (SSA), which considers the foraging feature of flying squirrels in search of food source. The filtered web pages make the proposed ranking algorithm to effectively perform well in web page re-ranking process. The features associated with the web documents are extracted and enable the re-ranking measure to find the ranking score using the fitness measure. The fitness with the minimal distance of retrieval is accepted as the best solution. However, the proposed SqSRank algorithm obtained better performance in terms of the metrics, like precision, F-measure, and recall, which acquired the values of 0.964, 0.980, and 0.996, respectively.