LARES: Integrating Memory-Based Collaborative Filtering and User Location Awareness for Tourism Recommendation
Received: 7 November 2025 | Revised: 20 December 2025 and 8 January 2026 | Accepted: 9 January 2026 | Online: 4 April 2026
Corresponding author: Triyanna Widiyaningtyas
Abstract
Travel recommendation algorithms on e-commerce websites are important for helping people choose the right destinations based on their preferences. However, collaborative filtering methods generally struggle with sparse data, scalability issues, and a lack of contextual awareness, which makes recommendations less accurate. This work presents the Location-Aware Recommendation System (LARES), a methodology that combines rank-based and geographical similarity through a weighted linear combination to address the sparsity problem. The spatial component includes the normalized geographical distance between users, controlled by an adjustable coefficient that balances its effect on rating similarity. The choice of public datasets from TripAdvisor and Yelp resulted in better LARES performance compared to traditional similarity models such as the Pearson Correlation Coefficient (PCC) and the EDJM method, which combines the Jeffries–Matusita distance and Jaccard Mean Squared Distance (JMSD), especially in addressing data sparsity issues. Although the Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) values for Yelp tended to be lower than those for TripAdvisor, this indicates that geographical similarity still has a strong influence on improving the accuracy and relevance of tourism recommendation systems, while introducing only limited additional computational cost.
Keywords:
tourism recommender system, collaborative filtering, location-aware recommender systemDownloads
References
C. Capineri and A. Romano, "The platformization of tourism: from accommodation to Experiences," Digital Geography and Society, vol. 2, Jan. 2021, Art. no. 100012. DOI: https://doi.org/10.1016/j.diggeo.2021.100012
Z. Z. Zarezadeh, P. Benckendorff, and U. Gretzel, "Online tourist information search strategies," Tourism Management Perspectives, vol. 48, Sept. 2023, Art. no. 101140. DOI: https://doi.org/10.1016/j.tmp.2023.101140
L. Xue and Y. Zhang, "The effect of distance on tourist behavior: A study based on social media data," Annals of Tourism Research, vol. 82, May 2020, Art. no. 102916. DOI: https://doi.org/10.1016/j.annals.2020.102916
L. Zhang, J. Guo, R. Kang, B. Zhao, C. Zhang, and J. Li, "Hotel Review Classification Based on the Text Pretraining Heterogeneous Graph Neural Network Model," Computational Intelligence and Neuroscience, vol. 2022, no. 1, Mar. 2022, Art. no. 5259305. DOI: https://doi.org/10.1155/2022/5259305
F. A. Panarto, T. M. Phanghegar, V. Gunardi, and T. W. Cenggoro, "Leveraging user-item interactions and hotel characteristics for hotel recommendations in Indonesia with graph neural networks," Procedia Computer Science, vol. 245, pp. 710–719, Jan. 2024. DOI: https://doi.org/10.1016/j.procs.2024.10.297
L. Salau, M. Hamada, R. Prasad, M. Hassan, A. Mahendran, and Y. Watanobe, "State-of-the-Art Survey on Deep Learning-Based Recommender Systems for E-Learning," Applied Sciences, vol. 12, no. 23, Nov. 2022, Art. no. 11996. DOI: https://doi.org/10.3390/app122311996
N. Jeong, J. Lee, N. Jeong, and J. Lee, "An Aspect-Based Review Analysis Using ChatGPT for the Exploration of Hotel Service Failures," Sustainability, vol. 16, no. 4, Feb. 2024, Art. no. 1640. DOI: https://doi.org/10.3390/su16041640
M. Bourgais, C. Zanni-Merk, R. Fatali, and N. Alizada, "Avoiding the Overspecialization of Recommender Systems in Tourism with Semantic Trajectories, Initial Thoughts," Procedia Computer Science, vol. 207, pp. 1933–1942, Jan. 2022. DOI: https://doi.org/10.1016/j.procs.2022.09.252
I. Kianinezhad, M. Bayati, A. Harounabadi, and D. Akbari, "Developing a Location-Based Recommender System Using Collaborative Filtering Technique in the Tourism Industry," Tehnički glasnik, vol. 16, no. 1, pp. 53–59, Feb. 2022. DOI: https://doi.org/10.31803//tg-20210706082307
J. Zhang, Y. Yuan, and A. Qu, "Tensor factorization recommender systems with dependency," Electronic Journal of Statistics, vol. 16, no. 1, pp. 2175–2205, Jan. 2022. DOI: https://doi.org/10.1214/22-EJS1978
A. Tholib, T. Widiyaningtyas, and D. D. Prasetya, "An Intelligent Recommendation System Utilizing a Hybrid Deep Learning Method," Engineering, Technology & Applied Science Research, vol. 15, no. 4, pp. 25971–25977, Aug. 2025. DOI: https://doi.org/10.48084/etasr.12230
H. Tahmasbi, M. Jalali, and H. Shakeri, "TSCMF: Temporal and social collective matrix factorization model for recommender systems," Journal of Intelligent Information Systems, vol. 56, no. 1, pp. 169–187, Feb. 2021. DOI: https://doi.org/10.1007/s10844-020-00613-w
T. Widiyaningtyas, I. Hidayah, and T. B. Adji, "Recommendation Algorithm Using Clustering-Based UPCSim (CB-UPCSim)," Computers, vol. 10, no. 10, Oct. 2021, Art. no. 123. DOI: https://doi.org/10.3390/computers10100123
H. Al-Bashiri, M. A. Abdulgabber, A. Romli, and F. Hujainah, "Collaborative Filtering Recommender System: Overview and Challenges," Advanced Science Letters, vol. 23, no. 9, pp. 9045–9049, Sept. 2017. DOI: https://doi.org/10.1166/asl.2017.10020
K. Lin, S. Yang, and S.-G. Na, "Collaborative Filtering Algorithm-Based Destination Recommendation and Marketing Model for Tourism Scenic Spots," Computational Intelligence and Neuroscience, vol. 2022, no. 1, Apr. 2022, Art. no. 7115627. DOI: https://doi.org/10.1155/2022/7115627
C. He and C. Hua, "Research on User Profile Combined with Collaborative Filtering Recommendation Algorithm for Intelligent Tourism," Academic Journal of Science and Technology, vol. 7, no. 1, pp. 63–69, Aug. 2023. DOI: https://doi.org/10.54097/ajst.v7i1.10990
Z. Wang, "Intelligent recommendation model of tourist places based on collaborative filtering and user preferences," Applied Artificial Intelligence, vol. 37, no. 1, Dec. 2023, Art. no. 2203574. DOI: https://doi.org/10.1080/08839514.2023.2203574
"Hotel Reviews." Kaggle. [Online]. Available: https://www.kaggle.com/datasets/datafiniti/hotel-reviews.
"Yelp Open Dataset." Yelp Data Licensing. [Online]. Available: https://business.yelp.com/data/resources/open-dataset/.
N. Dilekli, A. E. Janitz, J. E. Campbell, and K. M. de Beurs, "Evaluation of geoimputation strategies in a large case study," International Journal of Health Geographics, vol. 17, no. 1, July 2018, Art. no. 30. DOI: https://doi.org/10.1186/s12942-018-0151-y
A. Iftime, S. Omer, V.-A. Burcea, O. Călinescu, and R.-M. Babeș, "Spatial and Temporal Correlations of COVID-19 Mortality in Europe with Atmospheric Cloudiness and Solar Radiation," ISPRS International Journal of Geo-Information, vol. 14, no. 8, July 2025, Art. no. 283. DOI: https://doi.org/10.3390/ijgi14080283
P. Jomsri, D. Prangchumpol, K. Poonsilp, and T. Panityakul, "Hybrid recommender system model for digital library from multiple online publishers." F1000Research, Apr. 04, 2024. DOI: https://doi.org/10.12688/f1000research.133013.2
A. Noorian, A. Harounabadi, and R. Ravanmehr, "A novel Sequence-Aware personalized recommendation system based on multidimensional information," Expert Systems with Applications, vol. 202, Sept. 2022, Art. no. 117079. DOI: https://doi.org/10.1016/j.eswa.2022.117079
T. Widiyaningtyas, I. Hidayah, and T. B. Adji, "User profile correlation-based similarity (UPCSim) algorithm in movie recommendation system," Journal of Big Data, vol. 8, no. 1, Mar. 2021, Art. no. 52. DOI: https://doi.org/10.1186/s40537-021-00425-x
J. Li, H. Xue, Q. Tang, H. Wang, and T. Gao, "SABTR: semantic analysis-based tourism recommendation," Frontiers in Physics, vol. 12, Oct. 2024, Art. no. 1491365. DOI: https://doi.org/10.3389/fphy.2024.1491365
M. Hong and J. J. Jung, "Multi-criteria tensor model for tourism recommender systems," Expert Systems with Applications, vol. 170, May 2021, Art. no. 114537. DOI: https://doi.org/10.1016/j.eswa.2020.114537
J. Lee, J. A. Shin, D.-K. Chae, and S.-C. Lee, "Personalized Tour Recommendation via Analyzing User Tastes for Travel Distance, Diversity and Popularity," Electronics, vol. 11, no. 7, Apr. 2022, Art. no. 1120. DOI: https://doi.org/10.3390/electronics11071120
A. P. Wibawa, A. B. P. Utama, H. Elmunsyah, U. Pujianto, F. A. Dwiyanto, and L. Hernandez, "Time-series analysis with smoothed Convolutional Neural Network," Journal of Big Data, vol. 9, no. 1, Apr. 2022, Art. no. 44. DOI: https://doi.org/10.1186/s40537-022-00599-y
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Copyright (c) 2026 Mas Nurul Hamidah, Triyanna Widiyaningtyas, Wahyu Sakti Gunawan Irianto, Wahyu Caesarendra
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