Developing the remaining components using collaborative filtering and demographic-based methodologies

The gap

Evidence profile

Sourced from the future-work section and future work and recommendations of the source papers, classified as general, spanning 3 journals.

Supporting evidence — 3 representative gaps

• Combining Content-Based Filtering Methods for Building a Powerful Hybrid Recommender System to Improve Tourism in Drâa-Tafilalet Area (2026) · Technologies · doi

  develop the remaining components using collaborative filtering and demographic-based methodologies - integrate them into the Big Data architecture framework of the Hybrid Tourism Recommendation System - augment the database with additional points of interest (POIs) and recommend a trip itinerary based on the selected top-rated sites and activities

  generalfuture-work section
  Keywords: develop remaining components using collaborative filtering demographic-based methodologies
• A Proposed Hybrid Recommender System for Tourism Industry in Iraq Using Evolutionary Apriori and K-means Algorithms (2026) · Franklin Open · doi

  This study proposes a hybrid recommender system for tourism using evolutionary Apriori and K-means algorithms, demonstrating promising results in terms of accuracy and performance efficiency. We acknowledge that the use of artificially generated data limits the full validity and generalizability of the proposed system. This approach was necessitated by the lack of publicly available real-world tourism data specific to Iraq, which the system is designed to serve. Moving forward, we plan to collaborate with local tourism agencies or data providers to acquire real-world data, enabling a more comprehensive evaluation and refinement of the system's applicability across different contexts. Additionally, to clarify the methodology used, we provided details on data splitting, evaluation metrics, and validation processes. This will ensure a better understanding of the methods employed and enhance the system's reproducibility and reliability. We recognize that using artificially generated data may impact the accuracy and reliability of the system's evaluation. However, this approach was necessary due to the unavailability of comprehensive real-world tourism data specific to Iraq. Currently, no publicly accessible datasets exist that comprehensively capture visitor behaviors, site visitation patterns, and other tourism-related data for Iraq. In creating the synthetic data, we ensured it reflected realistic patterns by drawing from publicly available statistics and data trends from similar tourism contexts. The dataset was validated through distribution checks to align closely with typical tourism behaviors. Moving forward, as real-world data becomes available, we intend to validate our findings further to ensure even greater accuracy and applicability of the recommender system. Conflicts of interest/Competing interests: Not declared. Funding: Not received. Ethics approval: Not applicable. Consent to participate: Author voluntarily agrees to participate in this research study. Consent for publication: Not applicable. Availability of data and material: Available upon request. Code availability: Available upon request. Authors' contributions: Not applicable.

  generalfuture workevidence 5/5
  Keywords: system tourism available real world accuracy publicly iraq evaluation applicable recommender using artificially generated approach
• Sequential Tourism Recommendation Using Dual-Input LSTM for Sustainable Destination Distribution (2026) · Teknika · doi

  [22] Z. Nie and Z. Nie, “A multi-source behavioral data framework for interpretable urban tourism forecasting,” Sci. Rep., vol. 16, no. 1, p. 2257, 2025, doi: 10.1038/s41598-025-32127-2. [23] K. Zhang, Y. Chen, and C. Li, “Discovering the tourists’ behaviors and perceptions in a tourism destination by analyzing photos’ visual content with a computer deep learning model: The case of Beijing,” Tour. Manag., vol. doi: 75, https://doi.org/10.1016/j.tourman.2019.07.002. 595–608, 2019, pp. [24] S. Puttinaovarat, S. Chai-Arayalert, and W. Saetang, “GIS-Based Personalized Tourism Recommendation Using Association Rule Mining to Support Sustainable Tourism,” Sustainability, vol. 18, no. 6, 2026, doi: 10.3390/su18063145. [25] I. Gede, B. Arya Budaya, G. Putra, and M. Yusadara, “A Comparative Analysis of Character and Word-Based Tokenization for Kawi-Indonesian Neural Machine Translation,” Available: 2025. http://jurnal.polibatam.ac.id/index.php/JAIC [Online]. [26] Y. Zhang, Z. Lin, C. C. Tong, and S. W. Ho, “Enhancing patterns: tokenization cumulative logic for segmenting user-generated content in logographic languages,” J. Comput. Soc. Sci., vol. 8, no. 3, p. 80, 2025, doi: 10.1007/s42001-025-00406-7. accuracy with dynamic [27] W. Hu, Z. Huang, J. Cai, and X. Zhao, “Dual-temporal inflow–outflow dependency modeling for short-term metro outflow prediction,” PLoS One, vol. 21, no. 4, pp. e0347131-, Apr. [Online]. Available: https://doi.org/10.1371/journal.pone.0347131 2026, [28] R. Xie, “Deep learning-based enterprise operation forecasting and green production optimization under the BRI,” Discover Artificial Intelligence, vol. 6, no. 1, p. 82, 2025, doi: 10.1007/s44163-025-00755-2. [29] E. Celik and S. I. Omurca, “A Novel Framework Leveraging Social Media Insights to Address the Cold- Start Problem in Recommendation Systems,” Journal of

  generalrecommendationsevidence 5/5
  Keywords: tourism based framework forecasting zhang content deep learning https recommendation tokenization available online outflow journal

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