Bibliometric Analysis of Publications on Recommender Systems in Tourism: Web of Science Case

Year 2026, Issue: Advanced Online Publication

Murat Çuhadar

https://doi.org/10.30519/ahtr.1542430

Abstract

This study conducts a comprehensive bibliometric analysis of 495 scholarly publications on recommender systems (RSs) in tourism indexed in the Web of Science database, with the aim of mapping research trends, identifying influential contributions, and revealing key scholarly networks. The findings indicate that artificial intelligence (AI) and machine learning (ML) constitute the dominant technological drivers, with computer science and information systems serving as the leading disciplinary domains. Notably, there is a growing interdisciplinary integration with social sciences, environmental studies, and business-related research. Europe and Asia emerge as the primary contributors, particularly Spain, China, and Italy, which demonstrate strong research output and collaborative networks. Co-authorship and co-citation analyses reveal four major research clusters centred on hybrid recommendation models, deep learning approaches, and personalized user experience optimization. The results underscore the field’s accelerating interdisciplinary expansion and its concentration within specific global hubs. By systematically mapping the intellectual landscape, identifying existing research gaps, and outlining future research trajectories, this study offers valuable insights for academics, practitioners, and policymakers seeking to understand the evolution, current state, and potential impact of RSs within the tourism sector.

Keywords

recommender systems , tourism , bibliometric analysis , artificial intelligence , machine learning

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