Efficient query clustering and information retrieval using Sequenced User Search Pattern Query Optimization

The rapid growth of the internet has ushered in an era of unprecedented information availability. However, this expansion has also given rise to the formidable challenge of efficiently extracting relevant data from the vast expanse of web pages. Conventional methods have often fallen short, inundating users with an abundance of irrelevant content. In response to this pressing issue, this study introduces the innovative Spider Optimization Algorithm (SOA) built upon the foundation of Sequenced User Search Pattern Query Optimization (SUSPQO). SOA-SUSPQO represents a pioneering approach that harnesses the power of density-based clustering for the thorough analysis of web page content. By doing so, it significantly enhances the efficiency and precision of information retrieval processes. In a rigorous evaluation involving a user base ranging from 100 to 500 individuals, the results were nothing short of remarkable. The SOA-SUSPQO framework yielded exceptional outcomes, boasting a 95.6% information retrieval performance rate, a precision rate of 93.4%, and an impressive recall rate of 92.1% for 500 users. By showcasing the demonstrated superiority of the SOA-SUSPQO framework over existing methodologies, it contributes significantly to the advancement of information retrieval technologies. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.