Abstract :

The Rapid Growth Of Digital Tourism Platforms, Mobile Applications, Online Reviews, And Location-based Services Has Created A Large Volume Of Heterogeneous Tourism Information. Although This Information Provides Tourists With Numerous Choices, It Can Also Make Destination Selection And Travel Planning Difficult. This Paper Proposes An Adaptive Hybrid Recommender System For Smart Tourism Using User Preferences, Context And Artificial Intelligence To Provide More Personalized And Relevant Tourism Recommendations. The Proposed Framework Considers Explicit Preferences, Previous Interactions, Behavioral Patterns, Tourism Resource Characteristics, And Contextual Information To Develop A Comprehensive Understanding Of Individual Tourists. The Proposed System Combines Hybrid Recommendation Techniques With Artificial Intelligence And Context-aware Processing To Improve The Relevance Of Tourism Suggestions. Factors Such As Location, Available Travel Time, Budget, Weather Conditions, Activity Preferences, And Previously Visited Destinations Can Be Incorporated Into The Recommendation Process. Unlike Static Recommendation Approaches, The Proposed Framework Is Designed To Adapt Its Recommendations When User Interests Or Travel Conditions Change. It Also Supports Personalized Itinerary Generation By Selecting And Organizing Suitable Tourism Resources According To The Users Preferences And Practical Travel Constraints. The Proposed Framework Provides A Unified Approach For Intelligent Tourism Applications By Integrating Adaptive User Profiling, Contextual Information, Hybrid Recommendation, And Dynamic Travel Planning. It Aims To Reduce Information Overload, Improve Personalization, And Support Tourists In Making More Informed Travel Decisions. The Framework Can Be Implemented Using Scalable Big Data Technologies And Machine Learning Methods And Can Subsequently Be Evaluated Using Realworld Tourism Datasets And Appropriate Recommendation Performance Measures. The Study Provides A Foundation For Developing Adaptive And Intelligent Tourism Recommendation Systems Capable Of Responding To Changing User Preferences And Travel Environments.

Published:

15-9-2026

Issue:

Vol. 26 No. 9 (2026)

Page Nos:

246-253

Section:

Articles

License:

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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