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Design and Implementation of a Personalized Tourism Recommendation System Based on the Data Mining and Collaborative

Xiang Nan1, Kayo Kanato2, Xiaolan Wang3

  • 1Nantong Normal College, Nantong 226000, Jiangsu, China.

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Summary

This study introduces a collaborative mining and filtering process (CMFP) to enhance personalized tourism recommendations. CMFP improves data analysis and reduces processing overhead for efficient, context-aware travel solutions.

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Area of Science:

  • Artificial Intelligence
  • Computer Science
  • Tourism Informatics

Background:

  • Personalized tourism recommendation systems rely on data analysis for context-aware solutions.
  • Existing systems face challenges with data processing overhead and recommendation efficiency.

Purpose of the Study:

  • To introduce a novel collaborative mining and filtering process (CMFP) for personalized tourism recommendation systems.
  • To improve recommendation efficiency and reduce data processing overhead.

Main Methods:

  • Developed a harmonized collaborative process (CMFP) integrating data mining and filtering.
  • Utilized knowledge-based transfer learning for efficient large data analysis.
  • Collaboratively analyzed global and personal travel/expenditure data.

Main Results:

  • CMFP effectively reduces data processing overheads.
  • The process improves the recommendation ratio and accuracy.
  • Performance metrics include accuracy, data handling rate, mining time, and overhead.

Conclusions:

  • CMFP offers an efficient approach to personalized travel recommendations.
  • The system provides adaptable travel recommendations based on updated knowledge bases.
  • This method enhances the performance of data-driven tourism systems.