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Improving the Accuracy of Tourism Recommendation System Based on Neural Collaborative Filtering

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Observational analysis improves recommendation accuracy in tourism by integrating user ratings and reviews, indicating better predictions.

Combining user ratings and reviews significantly improves recommendation accuracy, resulting in more relevant tourist destination suggestions.
The best performing NCF model showed an RMSE of 0.892 and an HR@10 of 0.735, highlighting the effectiveness of combining data types.
Data preprocessing and text embedding were crucial steps in developing the neural collaborative filtering model tailored for tourism.
Findings support further advancements in artificial intelligence-based systems for personalized tourism experiences.

Astri et al. (2025) studied this question.

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Observational analysis improves recommendation accuracy in tourism by integrating user ratings and reviews, indicating better predictions.