Recommender Systems: Emerging Trends from Four Decades of Research Using Bibliometric Analysis and Transformer-Based Models

Recommender systems represent an essential infrastructure for digital platforms. To understand their evolution, we analyze 15,944 Web of Science publications (1980–2025) using bibliometric techniques, generative and transformer models for sentiment analysis and latent topic modeling. Our analysis yields three major findings. First, e-commerce recommendation research exhibits rapid growth in advanced representation techniques, with compound annual growth rates for contrastive learning (187%), graph neural networks (89%) and federated learning (72%). Second, algorithmic fairness and privacy preservation have emerged as critical research directions. Third, collaborative networks indicate a geographical shift, with Asia–Pacific regions becoming influential research hubs. The methodology integrates CAGR analysis with Latent Dirichlet Allocation (LDA, coherence score = 0.687) and BERTopic for thematic mapping and network analysis. Additionally, we employ sentiment analysis (VADER, TextBlob and a sentiment analysis pipeline from Hugging Face Transformers) and temporal heatmaps to capture research narratives. Topic modeling with LDA identifies five core themes: (1) Collaborative Filtering; (2) Machine Learning and Educational Systems; (3) Web Services and Business Applications; (4) Content and Multimedia Recommendations; (5) Graph Neural Networks and Advanced Models. BERTopic provides eight more nuanced themes based on semantics. Citation patterns follow the Pareto principle, where the top 1% of articles account for 29.1% of all citations, confirming a highly skewed impact distribution. Notably, established keywords show declining trajectories, indicating a methodological evolution toward newer, deep learning and generative AI-based paradigms.