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Research Article

Personalized Sports Health Recommendation System Assisted by Q-Learning Algorithm

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Received 26 Oct 2023, Accepted 11 Dec 2023, Published online: 08 Jan 2024

References

  • Abed-Alguni Bilal, H., & Ashraf, O. M. (2018). Double delayed Q-learning. International Journal of Artificial Intelligence, 16(2), 41–59.
  • Ackerman, K. E., Stellingwerff, T., Elliott-Sale, K. J., Baltzell, A., Cain, M., Goucher, K., Fleshman, L., & Mountjoy, M. L. (2020). # REDS (Relative Energy Deficiency in Sport): Time for a revolution in sports culture and systems to improve athlete health and performance. British Journal of Sports Medicine, 54(7), 369–370. https://doi.org/10.1136/bjsports-2019-101926
  • Bodepudi, H. (2020). Faster the slow running RDBMS Queries with spark framework. International Journal of Scientific and Research Publications (IJSRP), 10(11), 287–291. https://doi.org/10.29322/IJSRP.10.11.2020.p10735
  • Bremer, E., Liska, T. M., Arbour-Nicitopoulos, K. P., Best, K. L., & Sweet, S. N. (2023). Examining long-term motivational and behavioral outcomes of two physical activity interventions. The Journal of Spinal Cord Medicine, 46(5), 807–817. https://doi.org/10.1080/10790268.2022.2033935
  • Bui, V.-H., Hussain, A., & Kim, H.-M. (2020). Double deep $ Q $-learning-based distributed operation of battery energy storage system considering uncertainties. IEEE Transactions on Smart Grid, 11(1), 457–469. https://doi.org/10.1109/TSG.2019.2924025
  • Charbuty, B., & Abdulazeez, A. (2021). Classification based on decision tree algorithm for machine learning. Journal of Applied Science and Technology Trends, 2(01), 20–28. https://doi.org/10.38094/jastt20165
  • Cheng, P., Ma, C., Ding, M., Hu, Y., Lin, Z., Li, Y., & Vucetic, B. (2019). Localized small cell caching: A machine learning approach based on rating data. IEEE Transactions on Communications, 67(2), 1663–1676. https://doi.org/10.1109/TCOMM.2018.2878231
  • Chu, T. L., & Zhang, T. (2018). Motivational processes in Sport Education programs among high school students: A systematic. European Physical Education Review, 24(3), 372–394. https://doi.org/10.1177/1356336X17751231
  • Cui, G., Luo, J., & Wang, X. (2018). Personalized travel route recommendation using collaborative filtering based on GPS trajectories. International Journal of Digital Earth, 11(3), 284–307. https://doi.org/10.1080/17538947.2017.1326535
  • Cui, Z., Xu, X., Xue, F., Cai, X., Cao, Y., Zhang, W., & Chen, J. (2020). Personalized recommendation system based on collaborative filtering for IoT scenarios. IEEE Transactions on Services Computing, 13(4), 685–695. https://doi.org/10.1109/TSC.2020.2964552
  • Ertefaie, A., Mckay, J. R., Oslin, D., & Strawderman, R. L. (2021). Robust Q-learning. Journal of the American Statistical Association, 116(533), 368–381. https://doi.org/10.1080/01621459.2020.1753522
  • Feng, F., He, X., Wang, X., Luo, C., Liu, Y., & Chua, T.-S. (2019). Deep item-based collaborative filtering for top-n recommendation. ACM Transactions on Information Systems, 37(2), 1–30. https://doi.org/10.1145/3314578
  • González-Cutre, D., & Sicilia, Á. (2019). The importance of novelty satisfaction for multiple positive outcomes in physical education. European Physical Education Review, 25(3), 859–875. https://doi.org/10.1177/1356336X18783980
  • Good, V., Hughes, D. E., Kirca, A. H., & Mcgrath, S. (2022). A self-determination theory-based meta-analysis on the differential effects of intrinsic and extrinsic motivation on salesperson performance. Journal of the Academy of Marketing Science, 50(3), 586–614. https://doi.org/10.1007/s11747-021-00827-6
  • Gutierrez, J. P., & Lee, K. (2021). High rate Denial-of-Service attack detection system for cloud environment using flume and spark. Journal of Information Processing Systems, 17(4), 675–689. https://doi.org/10.3745/JIPS.03.0164
  • He, X., He, Z., Song, J., Liu, Z., Jiang, Y.-G., & Chua, T.-S. (2018). NAIS: Neural attentive item similarity model for recommendation. IEEE Transactions on Knowledge and Data Engineering, 30(12), 2354–2366. https://doi.org/10.1109/TKDE.2018.2831682
  • Hsieh, Y.-Z., & Lin, S.-S. (2020). Robotic arm assistance system based on simple stereo matching and Q-learning optimization. IEEE Sensors Journal, 20(18), 10945–10954. https://doi.org/10.1109/JSEN.2020.2993314
  • Hui, L., & Chin-Chung, T. (2021). A review of using machine learning approaches for precision education. Educational Technology & Society, 24(1), 250–266.
  • Kalajas-Tilga, H., Koka, A., Hein, V., Tilga, H., & Raudsepp, L. (2020). Motivational processes in physical education and objectively measured physical activity among adolescents. Journal of Sport and Health Science, 9(5), 462–471. https://doi.org/10.1016/j.jshs.2019.06.001
  • Kosuru, V. S. R., & Venkitaraman, A. K. (2022). Developing a deep Q-learning and neural network framework for trajectory planning. European Journal of Engineering and Technology Research, 7(6), 148–157. https://doi.org/10.24018/ejeng.2022.7.6.2944
  • Ma, Y., Geng, X., & Wang, J. (2021). A deep neural network with multiplex interactions for cold-start service recommendation. IEEE Transactions on Engineering Management, 68(1), 105–119. https://doi.org/10.1109/TEM.2019.2961376
  • Macintyre, P. D., Schnare, B., & Ross, J. (2018). Self-determination theory and motivation for music. Psychology of Music, 46(5), 699–715. https://doi.org/10.1177/0305735617721637
  • Monteiro-Guerra, F., Rivera-Romero, O., Fernandez-Luque, L., & Caulfield, B. (2019). Personalization in real-time physical activity coaching using mobile applications: A scoping review. IEEE Journal of Biomedical and Health Informatics, 24(6), 1738–1751. https://doi.org/10.1109/JBHI.2019.2947243
  • Neace, S. M., Hicks, A. M., Decaro, M. S., & Salmon, P. G. (2022). Trait mindfulness and intrinsic exercise motivation uniquely contribute to exercise self-efficacy. Journal of American College Health: J of ACH, 70(1), 13–17. https://doi.org/10.1080/07448481.2020.1748041
  • Patel, H. H., & Prajapati, P. (2018). Study and analysis of decision tree based classification algorithms. International Journal of Computer Sciences and Engineering, 6(10), 74–78. https://doi.org/10.26438/ijcse/v6i10.7478
  • Quadrana, M., Cremonesi, P., & Jannach, D. (2018). Sequence-aware recommender systems. ACM Computing Surveys, 51(4), 1–36. https://doi.org/10.1145/3190616
  • Rashid, A. H. (2021). Abdulazeez Adnan Mohsin. Reinforcement learning and modeling techniques: A review. International Journal of Science and Business, 5(3), 174–189. https://ijsab.com/wp-content/uploads/696
  • Rv, K., & Sannasi, G. (2022). Target User Specific Q-Learning (TUQL) personalized product recommendation. International Journal of Intelligent Systems and Applications in Engineering, 10(4), 582–589.
  • Sanchit, A. (2018). Modern web-development using reactjs. International Journal of Recent Research Aspects, 5(1), 133–137.
  • Saroja, C. (2019). Using spark, kafka and NIFI for future generation of ETL in IT industry. Journal of Innovation in Information Technology, 3(2), 11–14.
  • Shi, Q., Lam, H., Xiao, B., & Tsai, S. (2018). Adaptive PID controller based on Q‐learning algorithm. CAAI Transactions on Intelligence Technology, 3(4), 235–244. https://doi.org/10.1049/trit.2018.1007
  • Tan, C., Han, R., Ye, R., & Chen, K. (2020). Adaptive learning recommendation strategy based on deep Q-learning. Applied Psychological Measurement, 44(4), 251–266. https://doi.org/10.1177/0146621619858674
  • Torres-Ronda, L., Beanland, E., Whitehead, S., Sweeting, A., & Clubb, J. (2022). Tracking systems in team sports: A narrative review of applications of the data and sport specific analysis. Sports Medicine - Open, 8(1), 15. https://doi.org/10.1186/s40798-022-00408-z
  • Vasavi, S., Gokhale, A. A., Priyanka G, V. N. (2019). Framework for visualization of geospatial query processing by integrating Redis with Spark. International Journal of Natural Computing Research, 8(3):1–25. https://doi.org/10.4018/IJNCR.2019070101
  • Wang, W., Chen, J., Wang, J., Chen, J., Liu, J., & Gong, Z. (2020). Trust-enhanced collaborative filtering for personalized point of interests recommendation. IEEE Transactions on Industrial Informatics, 16(9), 6124–6132. https://doi.org/10.1109/TII.2019.2958696
  • Wu, S., Tang, Y., Zhu, Y., Wang, L., Xie, X., & Tan, T. (2019). Session-based recommendation with graph neural networks. [Proceedings of the AAAI Conference on Artificial Intelligence, 33(01), 346–353. https://doi.org/10.1609/aaai.v33i01.3301346
  • Xi, B., & Lei, D. (2022). Q-learning-based teaching-learning optimization for distributed two-stage hybrid flow shop scheduling with fuzzy processing time. Complex System Modeling and Simulation, 2(2), 113–129. https://doi.org/10.23919/CSMS.2022.0002
  • Xiao, W., & Hu, J. (2020). SWEclat: A frequent itemset mining algorithm over streaming data using Spark Streaming. The Journal of Supercomputing, 76(10), 7619–7634. https://doi.org/10.1007/s11227-020-03190-5
  • Zhang, Q., Lin, M., Yang, L. T., Chen, Z., Khan, S. U., & Li, P. (2019). A double deep Q-learning model for energy-efficient edge scheduling. IEEE Transactions on Services Computing, 12(5), 739–749. https://doi.org/10.1109/TSC.2018.2867482
  • Zheng, T., & Ding, M. (2023). Behavior-aware english reading article recommendation system using online distilled deep q-learning. Journal of Cases on Information Technology, 25(1), 1–21. https://doi.org/10.4018/JCIT.324102
  • Zhu, X., Jing, X.-Y., Wu, D., He, Z., Cao, J., Yue, D., & Wang, L. (2021). Similarity-maintaining privacy preservation and location-aware low-rank matrix factorization for QoS prediction based web service recommendation. IEEE Transactions on Services Computing, 14(3), 889–902. https://doi.org/10.1109/TSC.2018.2839741

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