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

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

References

1. Ahn, H., Kim, K.J., & Han, I. (2006). Mobile advertisement recommender system using collaborative filtering: MAR-CF. In: Proceedings of the 2006 Conference of the Korea Society of Management Information Systems, pp 709-715.
2. Alyari, F., & Navimipour, N.J. (2018). Recommender systems: A systematic review of the state-of-the-art literature and suggestions for future research. Kybernetes, 47(5), 985-1017. https://doi.org/10.1108/K-06-2017-0196
3. Arentze, T., Kemperman, A., & Aksenov, P. (2018). Estimating a latent-class user model for travel recommender systems. Information Technology & Tourism, 19, 61-82. https://doi.org/10.1007/s40558-018-0105-z
4. Aygün, C., & Yıldız, O. (2016). Development of content-based book recommendation system using genetic algorithm. 24th Signal Processing and Communication Application Conference (SIU), Proceedings Book (pp.1025-1028). https://doi.org/10.1109/SIU.2016.7495917
5. Bahramian, Z., Abbaspour, R. A., & Claramunt, C. (2018). Toward geospatial collaborative tourism recommender systems. In S. Chaudhuri, & N. Ray (Eds.), GIS Applications in the Tourism and Hospitality Industry (pp.212-248). Hershey, PA: IGI Global. https://doi.org/10.4018/978-1-5225-5088-4.ch010
6. Baig, M.Z., Khatoon, H., Raza, S.S., & Pasta, M.Q. (2019). Theoretical foundations for recommender Systems. In O. Khalid, S.U. Khan, & A.Y. Zomaya (Eds.), Big data recommender systems, Volume: 1. Algorithms, Architectures, Big Data, Security and Trust. (pp. 9-26). London: The Institution of Engineering and Technology. https://doi.org/10.1049/PBPC035F_ch2
7. Borràs, J., Moreno, A., & Valls, A. (2014). Intelligent tourism recommender systems: A survey. Expert Systems with Applications, 41(16), 7370-7389. https://doi.org/10.1016/j.eswa.2014.06.007
8. Braunhofer, M., Elahi, M., & Ricci, F. (2015). User personality and the new user problem in a context-aware point of interest recommender system. In I. Tussyadiah, & A. Inversini (Eds.), Information and Communication Technologies in Tourism (pp. 537-549). Springer, Cham. https://doi.org/10.1007/978-3-319-14343-9_39

9. Chaturvedi, R., Verma, S., Ali, F., & Kumar, S. (2024). Reshaping tourist experience with AI-enabled technologies: A comprehensive review and future research agenda. International Journal of Human–Computer Interaction, 40(18), 5517–5533. https://doi.org/10.1080/10447318.2023.2238353
10. Dongsheng, L., Jianxun L., Le, Z., Kan R., Tun, L., Tao, W., & Xing, X. (2024). Recommender systems: Frontiers and practices. Singapore: Springer Nature Pte Ltd. https://doi.org/10.1007/978-981-99-8964-5
11. Elahi, M., Khosh Kholgh, D., Kiarostami, M. S., Oussalah, M., & Saghari, S. (2023). Hybrid recommendation by incorporating the sentiment of product reviews. Information Sciences, 625, 738-756. https://doi.org/10.1016/j.ins.2023.01.051
12. Esmaeili, E., Mardani, S., Golpayegani, S.A.H., & Madar, Z.Z. (2020). A novel tourism recommender system in the context of social commerce. Expert Systems with Applications, 149, 113301. https://doi.org/10.1016/j.eswa.2020.113301
13. Garcia, I., Sebastia, L., & Onaindia, E. (2011). On the design of individual and group recommender systems for tourism. Expert Systems with Applications, 38(6), 7683-7692. https://doi.org/10.1016/j.eswa.2010.12.143
14. Gavalas, D., Konstantopoulos, C., Mastakas, K., & Pantziou, G. (2014). Mobile recommender systems in tourism. Journal of Network and Computer Applications, 39, 319-333. https://doi.org/10.1016/j.jnca.2013.04.006
15. Jalilvand, M.R., & Ghasemi, H. (2024). Augmented reality technology in tourism and hospitality research: a review from 2010 to 2024. Journal of Science and Technology Policy Management, Vol. ahead-of-print No. ahead-of-print. https://doi.org/10.1108/JSTPM-04-2024-0136
16. Kar, M. Roy, & S. Datta (2024). Recommender Systems: Algorithms and their Applications, Transactions on Computer Systems and Networks. Singapore: Springer Nature Pte Ltd.
17. Kar, P., Roy, M., & Datta, S. (2024a). Collaborative filtering and content-based systems. In P. Kar, M. Roy, & S. Datta (Eds.), Recommender Systems: Algorithms and their Applications, Transactions on Computer Systems and Networks (pp. 19-30). Singapore: Springer Nature Pte Ltd. https://doi.org/10.1007/978-981-97-0538-2_3
18. Kar, P., Roy, M., & Datta, S. (2024b). Overview of recommendation systems. In P. Kar, M. Roy, & S. Datta (Eds.), Recommender Systems: Algorithms and their Applications, Transactions on Computer Systems and Networks (pp. 11-17). Singapore: Springer Nature Pte Ltd. https://doi.org/10.1007/978-981-97-0538-2_2
19. Kirilenko, A. P., Stepchenkova, S. O., Kim, H., & Li, X. (Robert). (2018). Automated sentiment analysis in tourism: Comparison of approaches. Journal of Travel Research, 57(8), 1012-1025. https://doi.org/10.1177/0047287517729757
20. Knani, M., Echchakoui, S., & Ladhari, R. (2022). Artificial intelligence in tourism and hospitality: Bibliometric analysis and research agenda. International Journal of Hospitality Management, 107, 103317. https://doi.org/10.1016/j.ijhm.2022.103317
21. Ko, H., Lee, S., Park, Y., & Choi, A. (2022). A survey of recommendation systems: Recommendation models, techniques, and application fields. Electronics, 11(1), 141. https://doi.org/10.3390/electronics11010141
22. Kumar, P.P., Vairachilai, S., Potluri, S., & Mohanty, S.N. (2021). Recommender Systems: Algorithms and Applications. Oxon, UK: CRC Press.
23. Leong, L. Y., Hew, T. S., Tan, G. W. H., Ooi, K. B., & Lee, V. H. (2021). Tourism research progress–A bibliometric analysis of tourism review publications. Tourism Review, 76(1), 1-26. https://doi.org/10.1108/TR-11-2019-0449
24. Lu, J., Wu, D., Mao, M., Wang, W., & Zhang, G. (2015). Recommender system application developments: A survey. Decision Support Systems, 74, 12-32, https://doi.org/10.1016/j.dss.2015.03.008
25. Massimo, D., & Ricci, F. (2022). Building effective recommender systems for tourists. AI Magazine, 43(2), 209-24. https://doi.org/10.1002/aaai.12057
26. Mavrić, B., Öğretmenoğlu, M., & Akova, O. (2021). Bibliometric analysis of slow tourism. Advances in Hospitality and Tourism Research (AHTR), 9(1), 157-178. https://doi.org/10.30519/ahtr.794656
27. Moreno, A., Valls, A., Isern, D., Marin, L., & Borràs, J. (2013). SigTur/E-Destination: Ontology-based personalized recommendation of tourism and leisure activities, Engineering Applications of Artificial Intelligence, 26(1), 633-651. https://doi.org/10.1016/j.engappai.2012.02.014
28. Nilashi, M., bin Ibrahim, O., Ithnin, N., & Sarmin, N. H. (2015). A multi-criteria collaborative filtering recommender system for the tourism domain using Expectation Maximization (EM) and PCA-ANFIS. Electronic Commerce Research and Applications, 14(6), 542-562. https://doi.org/10.1016/j.elerap.2015.08.004
29. Niñerola, A., Sánchez-Rebull, M. V., & Hernández-Lara, A. B. (2019). Tourism research on sustainability: A bibliometric analysis. Sustainability, 11(5), 1377. https://doi.org/10.3390/su11051377
30. Ning, X., Desrosiers, C., & Karypis, G. (2015). A comprehensive survey of neighborhood-based recommendation methods. (pp 37-76) In: Ricci F., Rokach L. & Shapira B. (Eds.) Recommender Systems Handbook. Springer, Boston, MA. https://doi.org/10.1007/978-1-4899-7637-6_2
31. Noguera, J. M., Barranco, M. J., Segura, R. J., & Martínez, L. (2012). A mobile 3D-GIS hybrid recommender system for tourism. Information Sciences, 215, 37-52. https://doi.org/10.1016/j.ins.2012.05.010
32. Noorian, A. (2024). Integrating user reviews and risk factors from social networks in a multi-objective recommender system. Electronic Commerce Research. https://doi.org/10.1007/s10660-024-09944-0
33. Núñez, J. C. S., Gómez‐Pulido, J. A., & Ramírez, R.R. (2024). Machine learning applied to tourism: A systematic review. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 14(5), e1549. https://doi.org/10.1002/widm.1549
34. Renjith, S., Sreekumar, A., & Jathavedan, M. (2020). An extensive study on the evolution of context-aware personalized travel recommender systems. Information Processing & Management, 57(1), 102078. https://doi.org/10.1016/j.ipm.2019.102078
35. Ricci, F. (2022). Recommender systems in tourism. In Z. Xiang et al. (eds.), Handbook of e-Tourism (pp. 457-474). Switzerland: Springer Nature AG. https://doi.org/10.1007/978-3-030-05324-6_26-1
36. Ricci, F., Rokach L., & Shapira B. (2015). Recommender Systems: Introduction and Challenges. In F. Ricci, L. Rokach, & B. Shapira (Eds.), Recommender Systems Handbook (pp. 1-34). Boston, MA: Springer. https://doi.org/10.1007/978-1-4899-7637-6
37. Ricci, F., Rokach, L., & Shapira, B. (2022). Recommender Systems Handbook (3rd Ed.). Springer. https://doi.org/10.1007/978-1-0716-2197-4
38. Rosário, A. T., & Dias, J. C. (2024). Exploring the landscape of smart tourism: A systematic bibliometric review of the literature of the Internet of Things. Administrative Sciences, 14(2), 22. https://doi.org/10.3390/admsci14020022
39. Roy, D., & Dutta, M. A (2022). Systematic review and research perspective on recommender systems. Journal of Big Data, 9(59), 1-36. https://doi.org/10.1186/s40537-022-00592-5
40. Sampaio, C., Farinha, L., & Sebastião, J. R. (2023). Tourism industry at times of crisis: A bibliometric approach and research agenda. Journal of Hospitality and Tourism Insights, 6(4), 1464–1484. https://doi.org/10.1108/JHTI-08-2021-0223
41. Shekhar, S. (2023). Bibliometric analysis and literature review of mountain tourism. Advances in Hospitality and Tourism Research (AHTR), 11(2), 317-340. https://doi.org/10.30519/ahtr.1143501
42. Sinha, B.B., & Dhanalakshmi, R. (2022). Evolution of recommender paradigm optimization over time. Journal of King Saud University-Computer and Information Sciences, 34(4), 1047-1059. https://doi.org/10.1016/j.jksuci.2019.06.008
43. Solano-Barliza, A., Arregocés-Julio, I., Aarón-Gonzalvez, M., Zamora-Musa, R., De-La-Hoz-Franco, E., Escorcia-Gutierrez, J., & Acosta-Coll, M. (2024). Recommender systems applied to the tourism industry: A literature review. Cogent Business & Management, 11(1). https://doi.org/10.1080/23311975.2024.2367088
44. Subramaniyaswamy, V., Manogaran, G., Logesh, R., Vijayakumar, V., Chilamkurti, N., Malathi, D., & Senthilselvan, N. (2019). An ontology-driven personalized food recommendation in IoT-based healthcare system. The Journal of Supercomputing, 75(6), 3184-3216. https://doi.org/10.1007/s11227-018-2331-8 (Retracted article. See vol. 79, pg. 5847, 2023)
45. Wang, S., Wang, Y., Sivrikaya, F., Albayrak, S., & Anelli, V.W. (2023). Data science for next-generation recommender systems. International Journal of Data Science and Analytics, 16, 135-145. https://doi.org/10.1007/s41060-023-00404-w
46. Zhang, D. (2024). Automated Tourism Path Recommendation System Using Convolutional Neural Network based Bidirectional Long Short-Term Memory. 2024 Second International Conference on Data Science and Information System (ICDSIS), Hassan, India, 2024, pp. 1-5. https://doi.org/10.1109/ICDSIS61070.2024.10594175
47. Zhang, Q., Lu, J., & Jin, Y. (2021). Artificial intelligence in recommender systems. Complex & Intelligent Systems, 7, 439-457. https://doi.org/10.1007/s40747-020-00212-w