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@article{199045,
        author = {Nipun Garg and Dr. Ritu Gautam},
        title = {AI-Based Travel Recommendation and Cost Estimation},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {11},
        pages = {15201-15206},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=199045},
        abstract = {Travel planning is a decision-making process because of the presence of varied destinations, varied costs, seasonal changes, and personal preferences of the users. The conventional online travel systems are fragmented systems, and the users have to perform manual comparisons of varied destinations, hotels, and transport costs from different sources. This paper proposes an advanced AI-powered Travel Recommendation and Cost Estimation System that combines recommendation systems, machine learning algorithms for cost predictions, seasonal analysis, and itinerary planning into a single system. The proposed system employs content-based filtering, collaborative filtering, clustering algorithms, and regression algorithms to offer personalized destination recommendations and cost predictions. The proposed system is validated through simulations of travel scenarios, which show improved relevance and accuracy of recommendations over rule-based systems.},
        keywords = {Artificial Intelligence, Travel recommendation Systems, Machine learning, Itinerary generation, Cost estimation},
        month = {April},
        }