Title: Clustrosearch: a novel, intent-aware, and anomalous reducing meta-search engine optimisation algorithm based on user scoring system and clustering

Authors: Parsa Parsafar

Addresses: Faculty of Electrical and Computer Engineering, University of Mazandaran, Babolsar, Iran

Abstract: This paper introduces Clustrosearch, a novel meta-search engine optimisation algorithm integrating a machine learning-based user scoring system to enhance search result accuracy and efficiency. In response to the shortcomings of traditional search engines in delivering tailored content, Clustrosearch innovatively addresses these challenges by prioritising user-centric information retrieval. Unlike existing approaches, it focuses on optimising result relevance without explicit intent awareness, making it versatile across various search scenarios. Clustrosearch incorporates advanced anomaly reduction techniques to minimise the impact of outlier results, thereby enhancing the overall quality of search outcomes. This approach is evaluated through comprehensive benchmarking against established meta-search algorithms, demonstrating its capability to significantly reduce irrelevant results and improve retrieval precision. This research underscores its scalability and effectiveness in enhancing user satisfaction through advanced information filtering techniques.

Keywords: search engine optimisation; SEO; meta-search engine optimisation; optimisation; linear search; rank-biased overlap; RBO.

DOI: 10.1504/IJWET.2025.149266

International Journal of Web Engineering and Technology, 2025 Vol.20 No.3, pp.269 - 296

Received: 27 Feb 2024
Accepted: 18 Nov 2024
Published online: 21 Oct 2025 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article