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Zolfagharnasab, Mohammad Hossein, PourMohammadBagher, Latifeh, Bahrani, Mohammad. (1403). Intelligent Travel Recommendations Using Neural Collaborative Filtering for Touristic Landmarks of Iran. سامانه مدیریت نشریات علمی, 2(2), 119-147. doi: 10.22054/jdsm.2025.82861.1059
Mohammad Hossein Zolfagharnasab; Latifeh PourMohammadBagher; Mohammad Bahrani. "Intelligent Travel Recommendations Using Neural Collaborative Filtering for Touristic Landmarks of Iran". سامانه مدیریت نشریات علمی, 2, 2, 1403, 119-147. doi: 10.22054/jdsm.2025.82861.1059
Zolfagharnasab, Mohammad Hossein, PourMohammadBagher, Latifeh, Bahrani, Mohammad. (1403). 'Intelligent Travel Recommendations Using Neural Collaborative Filtering for Touristic Landmarks of Iran', سامانه مدیریت نشریات علمی, 2(2), pp. 119-147. doi: 10.22054/jdsm.2025.82861.1059
Zolfagharnasab, Mohammad Hossein, PourMohammadBagher, Latifeh, Bahrani, Mohammad. Intelligent Travel Recommendations Using Neural Collaborative Filtering for Touristic Landmarks of Iran. سامانه مدیریت نشریات علمی, 1403; 2(2): 119-147. doi: 10.22054/jdsm.2025.82861.1059

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

Journal of Data Science and Modeling
دوره 2، شماره 2 - شماره پیاپی 4، شهریور 2024، صفحه 119-147    اصل مقاله (2.06 M)
نوع مقاله: Research Manuscript
شناسه دیجیتال (DOI): 10.22054/jdsm.2025.82861.1059
نویسندگان
Mohammad Hossein Zolfagharnasab1؛ Latifeh PourMohammadBagher* 2؛ Mohammad Bahrani2
1MSC Student, Department of Mathematics, Statistics, and Computer Science, Allameh Tabataba'i University, Tehran, Iran
2Assistant Professor, Department of Mathematics, Statistics, and Computer Science, Allameh Tabatabai University, Tehran, Iran
چکیده
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.
کلیدواژه‌ها
Neural collaborative filtering؛ Matrix factorization؛ Recommender Systems؛ Deep Information Retrieval Systems؛ Intelligent Tourist Management
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تعداد مشاهده مقاله: 633
تعداد دریافت فایل اصل مقاله: 396


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