AI-Based Intelligent Tour Guiding and Tourism Recommendation System Specialized for Pune

Authors

  • Divya P. Solanki Zeal Institute of Business Administration, Computer Application & Research (ZIBACAR), Pune, Maharashtra, India
  • Shreyas H. Vanjare Department of MCA, Zeal Institute of Business Administration, Computer Application and Research (ZIBACAR), Narhe, Pune, India
  • Dr. Babasaheb Mohite Department of MCA, Zeal Institute of Business Administration, Computer Application and Research (ZIBACAR), Narhe, Pune, India
  • Sarika Kore Department of MCA, Zeal Institute of Business Administration, Computer Application and Research (ZIBACAR), Narhe, Pune, India
  • Dr. Pratiksha Kamble Department of MCA, Zeal Institute of Business Administration, Computer Application and Research (ZIBACAR), Narhe, Pune, India

DOI:

https://doi.org/10.63671/ijsesr.v2i4.163

Keywords:

Artificial Intelligence, Intelligent Tourism, Tourism Recommendation System, Retrieval-Augmented Generation, Recommended System

Abstract

Tourists visiting Pune rely on multiple disconnected sources — directories, map apps, review platforms — to plan a visit, making discovery slow and unreliable. This paper presents the design, implementation and evaluation framework of an integrated AI tourism platform for Pune combining a source-verified geospatial tourism database (PostgreSQL/PostGIS), a deterministic-first personalized recommendation engine, a deterministic itinerary-planning heuristic, and a Retrieval-Augmented Generation (RAG) conversational layer (Gemini + pgvector) that grounds AI answers in verified data. The system (Spring Boot/Angular) currently holds 18 verified places, 21 categories and an 8-chunk starter knowledge base; its geospatial and vector-search mechanics were live-verified, including a genuine indexing bug found and fixed. A synthetic Stage 1 dataset of 120 responses covering seventeen of the system's seeded Pune-area places was analyzed (synthetic data was used for Stage 1 owing to time constraints and will be replaced by real reviewer data in Stage 2); the illustrative results show high overall visit satisfaction (mean 3.87/5) and a strong preference for personalized recommendation (mean importance 4.30/5), alongside crowd comfort as the lowest-rated experience factor (mean 3.43/5), consistent with overcrowding being the most frequently reported visitor complaint. The paper's contribution is an architectural and methodological integration of verified tourism data, geospatial computing and grounded conversational AI for a single Indian city, evaluated with Stage 1 synthetic data and reported with an explicit account of what is implemented-and-verified versus proposed.

Author Biographies

  • Divya P. Solanki, Zeal Institute of Business Administration, Computer Application & Research (ZIBACAR), Pune, Maharashtra, India

    Department of MCA, Zeal Institute of Business Administration, Computer Application and Research (ZIBACAR), Narhe, Pune

  • Shreyas H. Vanjare, Department of MCA, Zeal Institute of Business Administration, Computer Application and Research (ZIBACAR), Narhe, Pune, India

    Department of MCA, Zeal Institute of Business Administration, Computer Application and Research (ZIBACAR), Narhe, Pune

References

Banerjee, A., Banik, P., & Wörndl, W. (2023). A review on individual and multistakeholder fairness in tourism recommender systems. Frontiers in Big Data, 6, Article 1168692. https://doi.org/10.3389/fdata.2023.1168692

Banerjee, A., Satish, A., & Wörndl, W. (2024). Enhancing tourism recommender systems for sustainable city trips using retrieval-augmented generation (arXiv:2409.18003). arXiv. https://doi.org/10.48550/arXiv.2409.18003

Bao, J., Zheng, Y., Wilkie, D., & Mokbel, M. (2015). Recommendations in location-based social networks: A survey. GeoInformatica, 19(3), 525–565. https://doi.org/10.1007/s10707-014-0220-8

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

Ji, Z., Lee, N., Frieske, R., Yu, T., Su, D., Xu, Y., Ishii, E., Bang, Y. J., Madotto, A., & Fung, P. (2023). Survey of hallucination in natural language generation. ACM Computing Surveys, 55(12), Article 248. https://doi.org/10.1145/3571730

Lewis, P., Perez, E., Piktus, A., Petroni, F., Karpukhin, V., Goyal, N., Küttler, H., Lewis, M., Yih, W.-t., Rocktäschel, T., Riedel, S., & Kiela, D. (2020). Retrieval-augmented generation for knowledge-intensive NLP tasks. In H. Larochelle, M. Ranzato, R. Hadsell, M. F. Balcan, & H. Lin (Eds.), Advances in neural information processing systems (Vol. 33, pp. 9459–9474). Curran Associates.

Orden-Mejía, M., Carvache-Franco, M., Huertas, A., Carvache-Franco, O., & Carvache-Franco, W. (2025). Analysing how AI-powered chatbots influence destination decisions. PLOS ONE, 20(3), Article e0319463. https://doi.org/10.1371/journal.pone.0319463

Sarkar, J. L., Majumder, A., Panigrahi, C. R., Roy, S., & Pati, B. (2023). Tourism recommendation system: A survey and future research directions. Multimedia Tools and Applications, 82(6), 8983–9027. https://doi.org/10.1007/s11042-022-12167-w

Vansteenwegen, P., Souffriau, W., Vanden Berghe, G., & Van Oudheusden, D. (2011). The City Trip Planner: An expert system for tourists. Expert Systems with Applications, 38(6), 6540–6546.

Zhao, S., King, I., & Lyu, M. R. (2016). A survey of point-of-interest recommendation in location-based social networks (arXiv:1607.00647). arXiv. https://doi.org/10.48550/arXiv.1607.00647

Vol 2 No 4

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Published

2026-10-09

Issue

Vol. 2 No. 4: October-December 2026

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Articles

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