Research Article

Applying RNN-Based Technology to Construct A Recommendation Mechanism for Fitness Venues

Chan-Sheng Kuo 1 * , Kuan-Yu Chu 1 *
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1 Department of Information Management, Shih Hsin University, Taipei City 116043, Taiwan* Corresponding Author
International Journal of Business Studies and Innovation, 5(4), December 2025, 17-30, https://doi.org/10.35745/ijbsi2025v05.04.0002
Submitted: 10 May 2025, Published: 30 December 2025
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ABSTRACT

This research is focused on developing an Android-based system that leverages RNN and deep learning technologies to deliver personalized gym recommendations. By analyzing user preferences, the system employs deep learning algorithms to make precise predictions and suggest the most appropriate fitness venues. Integrated with GPS location tracking, the system provides real-time, location-specific recommendations, enhancing the accuracy and user experience of these suggestions. The study uses web scraping techniques to gather data on gyms from publicly available Taiwanese government resources, creating a comprehensive database with detailed information including location, types of services, equipment, user ratings, and accessibility features. The system is anticipated to make three significant contributions: (1) improving personalized user experiences by allowing swift venue filtering based on user preferences, thus enhancing convenience and satisfaction; (2) creating a complete fitness venue database that supports diverse decision-making and query needs; (3) boosting the interpretability and accuracy of the prediction model by examining how different features affect the prediction process, providing transparency into the model’s function and performance. This platform is designed to streamline the selection process for fitness venues, and provide an innovative digital solution for the fitness industry, further promoting the widespread accessibility and convenience of a healthy lifestyle.
Keywords: Fitness venues, Wellness management, Recommendation mechanism, Recurrent neural network

CITATION (APA)

Kuo, C.-S., & Chu, K.-Y. (2025). Applying RNN-Based Technology to Construct A Recommendation Mechanism for Fitness Venues. International Journal of Business Studies and Innovation, 5(4), 17-30. https://doi.org/10.35745/ijbsi2025v05.04.0002

REFERENCES

  1. Alameen, A. (2022). Improving the accuracy of multi-valued datasets in agriculture using logistic regression and LSTM-RNN method. TEM Journal, 11(1), 454–462.
  2. Alsharef, A., Aggarwal, K., Sonia, et al. (2022). Review of ML and AutoML solutions to forecast time-series data. Archives of Computational Methods in Engineering, 29(7), 5297–5311.
  3. Astawa, I. N. G. A., Pradnyana, I. P. B. A., & Suwintana, I. K. (2022). Comparison of RNN, LSTM, and GRU methods on forecasting website visitors. Journal of Computer Science and Technology Studies, 4(2), 11–18.
  4. Baena-Arroyo, M. J., García-Fernández, J., Gálvez-Ruiz, P., et al. (2020). Analyzing consumer loyalty through service experience and service convenience: Differences between instructor fitness classes and virtual fitness classes. Sustainability, 12(3), 828.
  5. Bladh, G. H. (2022). Spatial bodies: Vulnerable inclusiveness within gyms and fitness venues in Sweden. Social Sciences, 11(10), 455.
  6. Cui, Q., Wu, S., Liu, Q., et al. (2018). MV-RNN: A multi-view recurrent neural network for sequential recommendation. IEEE Transactions on Knowledge and Data Engineering, 32(2), 317–331.
  7. Dewi, R. K., Sari, Y. A., Widodo, A. W., et al. (2020). Testing for recommendation method in m-health sports venue recommendation system. International Journal of Innovative Technology and Exploring Engineering (IJITEE), 9(6), 2143–2146.
  8. Gray, S. E., Keyzer, P., Norton, K., et al. (2015). The role of equipment, the physical environment and training practices in customer safety within fitness facilities: The perspectives of fitness industry employees. Journal of Fitness Research, 4(2), 26–33.
  9. Hopfield, J. J. (1982). Neural networks and physical systems with emergent collective computational abilities. Proceedings of the National Academy of Sciences of the United States of America, 79(8), 2554–2558.
  10. Iftikhar, N., Liu, X., Nordbjerg, F. E., et al. (2016). A prediction-based smart meter data generator. In Proceedings of the 2016 19th International Conference on Network-Based Information Systems (pp. 173–179). Ostrava, Czech Republic, September 7–9, 2016.
  11. Jiang, Y., Liu, Y., Liu, Z., et al. (2023). Spatial distribution characteristics of public fitness venues: An urban accessibility perspective. Sustainability, 15(1), 601.
  12. Kingma, D. P., & Ba, J. (2014). Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980.
  13. Kratzert, F., Klotz, D., Herrnegger, M., et al. (2019). Toward improved predictions in ungauged basins: Exploiting the power of machine learning. Water Resources Research, 55(12), 11344–11354.
  14. MacIntosh, E., & Doherty, A. (2007). Reframing the service environment in the fitness industry. Managing Leisure, 12(4), 273–289.
  15. Mozaffar, M., Paul, A., Al-Bahrani, R., et al. (2018). Data-driven prediction of the high-dimensional thermal history in directed energy deposition processes via recurrent neural networks. Manufacturing Letters, 18, 35–39.
  16. Piotrowski, D., & Piotrowska, A. I. (2021). Operation of gyms and fitness clubs during the COVID-19 pandemic-financial, legal, and organisational conditions. Journal of Physical Education and Sport, 21, 1021–1028.
  17. Research and Markets. (2023). Global gym and health clubs market 2024–2028. TechNavio. Available online: https://www.researchandmarkets.com/ (accessed on 26 March 2025).
  18. Rimmer, J. H., Riley, B., Wang, E., et al. (2005). Accessibility of health clubs for people with mobility disabilities and visual impairments. American Journal of Public Health, 95(11), 2022–2028.
  19. Sampaio, A. R., Pimenta, N. J., Machado, M., et al. (2020). Development and validation of the Fitness Coaching Behavior Scale: Factor structure, validity and reliability. Retos: Nuevas tendencias en educación física, deporte y recreación, 37, 687–693.
  20. Shewalkar, A., Nyavanandi, D., & Ludwig, S. A. (2019). Performance evaluation of deep neural networks applied to speech recognition: RNN, LSTM and GRU. Journal of Artificial Intelligence and Soft Computing Research, 9(4), 235–245.
  21. Sports Administration, Ministry of Education, Taiwan. (2020). 2020 sports status survey results: Successful epidemic prevention and joyful exercise, diverse sports iTaiwan. Sports for All Division.
  22. Sports Administration, Ministry of Education, Taiwan. (2023a). 2023 sports statistics. Jiabin Co., Ltd.
  23. Sports Administration, Ministry of Education, Taiwan. (2023b). 112th year sports status survey: Final report. Shih Hsin University.
  24. Sports Administration, Ministry of Education, Taiwan. (2024). Venue information – Sports venues. National Sports Venue Information Networks-iPlay. Available online: http://data.gov.tw/?q=principle (accessed on 26 March 2025).
  25. Vlachas, P. R., Pathak, J., Hunt, B. R., et al. (2020). Backpropagation algorithms and reservoir computing in recurrent neural networks for the forecasting of complex spatiotemporal dynamics. Neural Networks, 126, 191–217.
  26. Wang, D., Wang, X., & Lv, S. (2019). End-to-end Mandarin speech recognition combining CNN and BLSTM. Symmetry, 11(5), 644.
  27. Yang, F., Jin, L., Lai, S., et al. (2019). Fully convolutional sequence recognition networks for water meter number reading. IEEE Access, 7, 11679–11687.
  28. Zhu, L. (2023). Research on legal liability and prevention mechanism of accidental injury accidents in commercial fitness clubs. Academic Journal of Management and Social Sciences, 4(3), 34–39.