IJCOPE Journal

UGC Logo DOI / ISO Logo

International Journal of Creative and Open Research in Engineering and Management

A Peer-Reviewed, Open-Access International Journal Supporting Multidisciplinary Research, Digital Publishing Standards, DOI Registration, and Academic Indexing.
Journal Information
ISSN: 3108-1754 (Online)
Crossref DOI: Available
ISO Certification: 9001:2015
Publication Fee: 599/- INR
Compliance: UGC Journal Norms
License: CC BY 4.0
Peer Review: Double Blind
Volume 02, Issue 04

Published on: April 2026

SMARTTOUR: AN EXPLAINABLE ML-BASED TOURIST RECOMMENDATION SYSTEM

G. Akshaya M.Avinash SK.Anwar pasha A.Sushanth

K. Kiran

Department of CSE (Data Science) ACE Engineering College Hyderabad Telangana India
DOI:https://doi.org/10.55041/ijcope.v2i4.054

Article Status

Plagiarism Passed Peer Reviewed Open Access

Available Documents

Download PDF Review Report

Abstract

Smartour is an explainable machine learning–based tourist recommendation system designed to provide personalized travel suggestions by leveraging user preferences, travel history, budget, and contextual factors such as location and season. Various machine learning models, including hybrid approaches, were implemented and optimized using data preprocessing and feature selection techniques to enhance performance. Explainability methods were integrated to ensure transparency and help users understand the reasoning behind recommendations, thereby increasing trust in the system. The model achieved an accuracy of 92%, demonstrating its effectiveness in improving user experience and supporting better travel decision-making. Additionally, the system adapts to dynamic user interests and incorporates feedback to continuously refine recommendations, enabling users to discover suitable destinations while reducing planning effort and contributing to more intelligent and user-centric tourism solutions.

How to Cite this Paper

Akshaya, G., M.Avinash, , pasha, S. & A.Sushanth, (2026). Smarttour: An Explainable Ml-Based Tourist Recommendation System. International Journal of Creative and Open Research in Engineering and Management, <i>02</i>(04). https://doi.org/10.55041/ijcope.v2i4.054

Akshaya, G., et al.. "Smarttour: An Explainable Ml-Based Tourist Recommendation System." International Journal of Creative and Open Research in Engineering and Management, vol. 02, no. 04, 2026, pp. . doi:https://doi.org/10.55041/ijcope.v2i4.054.

Akshaya, G., M.Avinash,SK.Anwar pasha, and A.Sushanth. "Smarttour: An Explainable Ml-Based Tourist Recommendation System." International Journal of Creative and Open Research in Engineering and Management 02, no. 04 (2026). https://doi.org/https://doi.org/10.55041/ijcope.v2i4.054.

Search & Index

Google Scholar Crossref DOI ResearchGate

References


  1. Smith and L. Wang, "Personalized Tourist Recommendation using ML," Collaborative Filtering, Content- Based Filtering, Cold-start problem, sparse data, 2018.

  2. Kumar and R. Singh, "Hybrid Travel Recommendation System," Decision Trees + Collaborative Filtering, Computationally expensive, scalability issues, 2019.

  3. Chen and M. Li, "ML-Based Tourist Destination Prediction," Random Forest, SVM, Limited context awareness, lacks explainability, 2020.

  4. Patel and K. Rao, "Explainable Travel Recommender," Gradient Boosting, SHAP for interpretability, Longer runtime, requires feature engineering, 2021.

  5. Johnson and T. Kim, "Personalized Trip Planning System," Neural Networks, Content-Based Filtering, Needs large dataset, low transparency, 2021.

  6. Singh and V. Mehta, "Context-Aware Tourist Recommendation," Random Forest + Contextual Features, Sensitive to feature selection, complexity, 2022.

  7. Chen and S. Verma, "Smart Tourist Recommendation Platform," Hybrid ML (Collaborative + Content- Based), Cold-start, handling imbalanced preferences, 2022.

  8. Gupta and A. Roy, "AI-Powered Travel Suggestion System," Deep Neural Networks, Requires extensive training data, low interpretability, 2023.

  9. Das and P. Sinha, "Explainable ML for Tourism," Random Forest, SHAP, Limited real-time deployment, 2023.

Ethical Compliance & Review Process

  • •All submissions are screened under plagiarism detection.
  • •Review follows editorial policy.
  • •Authors retain copyright.
  • •Peer Review Type: Double-Blind Peer Review
  • •Published on: Apr 04 2026
CCBYNC

This article is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. You are free to share and adapt this work for non-commercial purposes with proper attribution.

View License
Back to Volume 02, Issue 04View All Issues Next Article

← Previous Article

Smart Energy Optimization of Hybrid Renewable System Using Iot and Storage Technology

Next Article →

Study on Effectiveness of Recruitment Tools at Virtusa
Scroll to Top