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Deep neural collaborative filtering model for personalized travel recommendation.

K Aarif1, J Deepika2, M Ashwin Kumar1

  • 1School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India.

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|January 13, 2026
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Summary
This summary is machine-generated.

This study introduces a Neural Collaborative Filtering (NCF) model to enhance personalized travel recommendations. The NCF model significantly improves accuracy and user satisfaction by overcoming limitations of traditional methods.

Keywords:
Data sparsityDeeper learningNeural collaborative filteringReal-time recommendationsTravel suggestionsUser preferences

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Recommender Systems

Background:

  • Personalized travel recommendations are crucial for user experience.
  • Traditional collaborative filtering methods face challenges like data sparsity and cold start issues.
  • These limitations lead to suboptimal travel predictions and user satisfaction.

Purpose of the Study:

  • To develop an advanced personalized travel recommendation system.
  • To address diverse user preferences in travel planning using a novel approach.
  • To overcome the limitations of traditional collaborative filtering models.

Main Methods:

  • Implementation of a Neural Collaborative Filtering (NCF) model.
  • Utilizing a neural network to learn complex user-travel relationships.
  • Employing a multi-layer perceptron for refined predictions based on user interactions.

Main Results:

  • The NCF model significantly outperforms traditional recommendation methods.
  • Demonstrated improvements in prediction accuracy and user satisfaction.
  • Effective handling of data sparsity and diverse user preferences.

Conclusions:

  • The proposed NCF model advances personalized travel recommendations.
  • Neural networks offer a powerful solution for complex user-travel dynamics.
  • The system enhances user experience through more accurate and diverse travel suggestions.