Journal of Data Science and Modeling

Intelligent Travel Recommendations Using Neural Collaborative Filtering for Touristic Landmarks of Iran

Document Type : Research Manuscript

Authors

Mohammad Hossein Zolfagharnasab 1
Latifeh PourMohammadBagher 2
Mohammad Bahrani 2

1 MSC Student, Department of Mathematics, Statistics, and Computer Science, Allameh Tabataba'i University, Tehran, Iran

2 Assistant Professor, Department of Mathematics, Statistics, and Computer Science, Allameh Tabatabai University, Tehran, Iran

https://doi.org/10.22054/jdsm.2025.82861.1059
Abstract
This study introduces a tailored recommendation system aimed at enriching Iran’s tourism sector. Using a hybrid model that combines neural collaborative filtering (NCF) with matrix factorization (MF), our approach leverages both demographic and contextual data of combined tourist-landmark (4177 samples) to provide personalized touristic recommendations. Empirical evaluations on the implemented methods show that the hybrid model outperforms factorization techniques, achieving a test F1 score of 0.84, accuracy of 0.90, and a test error reduction from 0.83 to 0.37. Feature vector integration further improved test recall by 17%, underscoring the model's robustness in capturing user-item relationships. Further analysis using t-SNE as well as visual analyses of embedding structures confirm the systems ability to generalize patterns in latent space; thereby, mitigating cold-start problem for new tourists or unvisited landmarks. This study also contributes a structured dataset of Iranian landmarks, user ratings, and supplementary contextual data for fostering future research in culturally specific intelligent recommender systems. For implementation details, refer to the GitHub repository at https://github.com/MsainZn/Collaborative_Filtering_Tourism_Landmarks.

Keywords

Neural collaborative filtering
Matrix factorization
Recommender Systems
Deep Information Retrieval Systems
Intelligent Tourist Management
Subjects

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