Abstract :

The Rapid Expansion Of Digital Travel Platforms Has Led To A Significant Increase In Unstructured Textual Data Such As User Reviews, Search Queries, And Feedback. This Data Contains Valuable Insights For Understanding User Preferences And Improving Travel Recommendation Systems. However, Extracting Meaningful Information From Such Large And Diverse Data Sources Remains A Challenging Task. Traditional Approaches Rely On Manual Filtering, Static Rules, Or Basic Keyword Matching, Which Fail To Capture Contextual Meaning And User Intent. As A Result, These Methods Often Produce Generic Recommendations With Limited Accuracy And Adaptability. The Primary Challenge Lies In Transforming Unstructured Textual Data Into Structured, Meaningful Representations That Can Support Personalized Decision-making. Existing Methods Struggle With Handling Noisy Data, Identifying Semantic Relationships, And Adapting To Dynamic User Behavior. These Limitations Reduce The Effectiveness Of Recommendation Systems And Highlight The Need For Advanced Data-driven Techniques That Can Process Textual Information Efficiently And Accurately. To Address This, The Proposed System Utilizes Natural Language Processing Combined With Machine Learning To Generate Personalized Travel Recommendations. The Workflow Includes Data Preprocessing Steps Such As Tokenization, Normalization, Stopword Removal, And Lemmatization To Improve Text Quality. Feature Extraction Is Performed Using TF-IDF To Convert Textual Data Into Weighted Numerical Vectors. Machine Learning Models Including Logistic Regression, Support Vector Machine, Artificial Neural Networks, And Extra Trees Are Applied To Learn Patterns And Classify User Preferences Effectively. This Approach Enhances Recommendation Accuracy By Capturing User Intent And Contextual Relevance From Textual Data. It Provides Scalable And Efficient Processing While Improving Personalization And Decision-making. The System Contributes To Intelligent Travel Recommendation Solutions By Addressing The Limitations Of Traditional Methods And Enabling More Relevant And User-centric Outcomes.

Published:

24-4-1-2026

Issue:

Vol. 26 No. 4-1 (2026)

Page Nos:

846-856

Section:

Articles

License:

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

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