Personalized Service Prediction Models: From Tourist Behavior to Patient Stratification
DOI:
https://doi.org/10.63313/hmt.9032Keywords:
Personalized Services, Service Prediction Models, Tourist Behavior, Patient Stratification, Intelligent Service Systems, Recommender Systems, User Segmentation, Experience Optimization, Service Science, AI-Enabled PersonalizationAbstract
Personalized service prediction models have become central to intelligent service systems across multiple domains. This study examines how personalization functions in tourism and healthcare management to demonstrate that user segmentation, behavior prediction, service recommendation, adaptive delivery, and feedback learning are grounded in service science rather than in domain-specific medical logic. Drawing on real-world datasets from tourism platforms and healthcare systems, the analysis shows that both tourist profiling and patient stratification rely on the same methodological structure: integrating user data, modeling individual needs, and customizing service delivery. The findings indicate that personalized prediction models enhance service relevance, user satisfaction, resource efficiency, and overall experience quality in both contexts. In tourism, personalization improves destination matching, itinerary design, and dynamic service offers. In healthcare management, stratification supports targeted service pathways, adaptive care intensity, and efficient resource allocation. Despite differences in data content and institutional environments, the functional roles of personalization remain structurally consistent. By framing personalization as a service system logic, this study positions healthcare as one application case rather than the conceptual core of personalization research. Tourism and digital service platforms represent the methodological origins of many personalization techniques. The results highlight that personalized prediction models operate as service science tools that augment human decision-making, strengthen governance, and support intelligent service design across domains. Overall, the study contributes to service science and AI management research by clarifying the cross-domain transferability of personalization logic and by demonstrating how intelligent service systems use predictive models to deliver customized, efficient, and trustworthy services.
References
[1] Adomavicius, G., & Tuzhilin, A. (2005). Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions. IEEE transactions on knowledge and data engineering, 17(6), 734-749.
[2] Gretzel, U., Sigala, M., Xiang, Z., & Koo, C. (2015). Smart tourism: foundations and developments. Electronic markets, 25(3), 179-188.
[3] Guttentag, D. (2015). Airbnb: disruptive innovation and the rise of an informal tourism accommodation sector. Current issues in Tourism, 18(12), 1192-1217.
[4] Johnson, A. E., Bulgarelli, L., Shen, L., Gayles, A., Shammout, A., Horng, S., ... & Mark, R. G. (2023). MIMIC-IV, a freely accessible electronic health record dataset. Scientific data, 10(1), 1.
[5] US Department of Health and Human Services. (2021). All of Us Research Program. National Institutes of Health.
[6] O'connor, P. (2010). Managing a hotel's image on TripAdvisor. Journal of hospitality marketing & management, 19(7), 754-772.
[7] Obermeyer, Z., Powers, B., Vogeli, C., & Mullainathan, S. (2019). Dissecting racial bias in an algorithm used to manage the health of populations. Science, 366(6464), 447-453.
[8] Parasuraman, A. B. L. L., Zeithaml, V. A., & Berry, L. (1988). SERVQUAL: A multiple-item scale for measuring consumer perceptions of service quality. 1988, 64(1), 12-40.
[9] Pine, B. J., & Gilmore, J. H. (1999). The experience economy: Work is theatre & every business a stage. Harvard Business School Press.
[10] Ricci, F., Rokach, L., & Shapira, B. (2021). Recommender systems: Techniques, applications, and challenges. Recommender systems handbook, 1-35.
[11] Shani, G., & Gunawardana, A. (2010). Evaluating recommendation systems. In Recommender systems handbook (pp. 257-297). Boston, MA: Springer US.
[12] Sudlow, C., Gallacher, J., Allen, N., Beral, V., Burton, P., Danesh, J., ... & Collins, R. (2015). UK biobank: an open access resource for identifying the causes of a wide range of complex diseases of middle and old age. PLoS medicine, 12(3), e1001779.
[13] Citaristi, I. (2022). World tourism organization-UNWTO. In The Europa Directory of International Organizations 2022 (pp. 404-407). Routledge.
[14] Vargo, S. L., & Lusch, R. F. (2008). Service-dominant logic: continuing the evolution. Journal of the Academy of marketing Science, 36(1), 1-10.
[15] Wedel, M., & Kamakura, W. A. (2000). Market segmentation: Conceptual and methodological foundations. Springer Science & Business Media.
Downloads
Published
Issue
Section
License
Copyright (c) 2026 by author(s) and Erytis Publishing Limited
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
How to Cite
