AQST-ClustNet: Hybrid Aquila Quantum Sooty Tern Optimization for User Profile Clustering in Social Network

Abstract

User profile clustering is the process of grouping users on social media sites based on common characteristics identified in their profile data, such as demographics, interests, and interactions. Profile clustering allows users to engage in targeted marketing, skill-matching, and collaborative networking by grouping them based on similar attributes, interests, or professional criteria. However, one key drawback of user profile clustering is its sensitivity to noisy and missing data, high-dimensional feature spaces, poor semantic understanding, and complexity limitations. To overcome these issues, a novel Hybrid Aquila Quantum Sooty tern optimization for clustering (AQST-ClustNet) approach based on user profiles (UP) has been proposed in this paper. The user profile data is preprocessed using NLP techniques involving data stemming, handling of missing data or values, removal of stop words, and data extraction for eliminating inappropriate data. A Hybrid Aquila Quantum Sooty Tern Optimization (HAQSTO) algorithm is employed for clustering the user profile into healthcare professionals, marketing professionals, software developers, and educators. The efficiency of the developed method is assessed employing various metrics, including Calinski–Harabasz score (CHS), Silhouette score (SHS), and Davies–Bouldin score (DBS). The proposed model achieves less runtime of 45 s, whereas the existing techniques, such as MCEMS, DBSTexC, and TSMIUSC-Miner, achieve runtimes of 70 s, 79 s, and 60 s. Using the effective dual-stage feature extraction and clustering approach, the complexity of clustering and a high-dimensional feature space is effectively reduced.