Personalized Recommendation Algorithm of Piano Practice Scheme Based on Multi Objective Optimization

Chunping Feng1
1 Music Academy, Guangzhou Xinhua University, Guangzhou, Guangdong, China
International Scientific Technical and Economic Research 2026, Vol. 4, No. 1, pp. 245-265
DOI: 10.71451/ISTAER2612
Received: 16 January 2026; Revised: 22 February 2026; Accepted: 25 March 2026; Published: 31 March 2026
Abstract

Aiming at the problem that traditional recommendation methods in piano practice struggle to balance multiple optimization objectives—such as skill improvement, time cost, user matching, and cognitive load—this paper proposes a personalized practice scheme recommendation algorithm based on multi-objective optimization. The algorithm first constructs a multi-dimensional user capability vector model and tracks the evolution of learners' skill dimensions, such as rhythm control and fingering proficiency, in real time through a dynamic state update mechanism. On this basis, the exercise recommendation is formalized as a four-objective optimization problem, the Pareto optimal theory is used to solve the non-dominated solution set, and a heuristic search strategy integrating sequence dependencies is designed to generate a coherent exercise path. Based on the data set containing 1248 users and 120000 interactive records, the experimental verification shows that the skill improvement rate, recommendation efficiency, and user satisfaction index of this method reach 0.513, 0.603, and 0.624 respectively, and the comprehensive score is 10.8% higher than that of the optimal baseline NSGA-II. The ablation experiment further confirms that the multi-objective optimization module contributes the most (the performance is reduced by 9.9%). The proposed method effectively realizes the unification of multi-objective collaborative optimization and personalized recommendation.

Keywords
Multi objective optimization Personalized recommendation Piano practice Intelligent music education Sequence modeling
References
  1. Qian, L. (2025). Research and practice on instructional methods for piano improvization based on computer technology. International Journal of High Speed Electronics and Systems, 34(02), 2440080. DOI: 10.1142/S0129156424400809
  2. Allingham, E., & Wöllner, C. (2022). Slow practice and tempo-management strategies in instrumental music learning: Investigating prevalence and cognitive functions. Psychology of Music, 50(6), 1925-1941. DOI: 10.1177/03057356211073481
  3. Wang, K., & Zhang, P. (2025). Enhancing opera vocal education through advanced machine learning algorithms: analytics for talent development and curriculum design. Interactive Learning Environments, 33(10), 5923-5943. DOI: 10.1080/10494820.2025.2488991
  4. Lin, F., & Chen, F. (2025). Intelligent music education system: utilizing algorithms for personalized learning experience. International Journal of High Speed Electronics and Systems, 2540253. DOI: 10.1142/S0129156425402530
  5. Xiong, Z. (2025). Exploring the potential and challenges of applying multiple intelligences theory in Chinese music education. Teaching and Teacher Education, 160, 105031. DOI: 10.1016/j.tate.2025.105031
  6. Howie, E. E., Dharanikota, H., Gunn, E., Ambler, O., Dias, R., Wigmore, S. J., ... & Yule, S. (2023). Cognitive load management: an invaluable tool for safe and effective surgical training. Journal of Surgical Education, 80(3), 311-322. DOI: 10.1016/j.jsurg.2022.12.010
  7. Castro-Alonso, J. C., De Koning, B. B., Fiorella, L., & Paas, F. (2021). Five strategies for optimizing instructional materials: Instructor-and learner-managed cognitive load. Educational Psychology Review, 33(4), 1379-1407. DOI: 10.1007/s10648-021-09606-9
  8. Sortwell, A., Gkintoni, E., Díaz-García, J., Ellerton, P., Ferraz, R., & Hine, G. (2026). Beyond cognitive load theory: Why learning needs more than memory management. Brain Sciences, 16(1), 109. DOI: 10.3390/brainsci16010109
  9. Fang, H., Zhang, D., Shu, Y., & Guo, G. (2020). Deep learning for sequential recommendation: Algorithms, influential factors, and evaluations. ACM Transactions on Information Systems (TOIS), 39(1), 1-42. DOI: 10.1145/3426723
  10. Ahmadian Yazdi, H., Seyyed Mahdavi, S. J., & Ahmadian Yazdi, H. (2024). Dynamic educational recommender system based on Improved LSTM neural network. Scientific Reports, 14(1), 4381. DOI: 10.1038/s41598-024-54729-y
  11. Shaban, A., Pearson, E., & Chang, V. (2021). Evaluation of user experience, cognitive load, and training performance of a gamified cognitive training application for children with learning disabilities. Frontiers in Computer Science, 3, 617056. DOI: 10.1145/3371300.3383341
  12. Skulmowski, A., & Xu, K. M. (2022). Understanding cognitive load in digital and online learning: A new perspective on extraneous cognitive load. Educational Psychology Review, 34(1), 171-196. DOI: 10.1007/s10648-021-09624-7
  13. Merchán Sánchez-Jara, J. F., González Gutiérrez, S., Cruz Rodríguez, J., & Syroyid Syroyid, B. (2024). Artificial intelligence-assisted music education: A critical synthesis of challenges and opportunities. Education Sciences, 14(11), 1171. DOI: 10.3390/educsci14111171
  14. Lu, L. (2025). Ai-powered intelligent music education systems for real-time feedback and performance assessment. International Journal of Information and Communication Technology, 26(18), 33-47. DOI: 10.1504/IJICT.2025.146690
  15. Ziegenbein, N., Friedman, J., & Moringen, A. (2022). Monitoring the Learning Progress In Piano Playing With Hidden Markov Models. In Adjunct Proceedings of the 30th ACM Conference on User Modeling, Adaptation and Personalization (pp. 335-341). DOI: 10.1145/3511047.3537666
  16. Fu, L., & Ma, X. (2021). An improved recommendation method based on content filtering and collaborative filtering. Complexity, 2021(1), 5589285. DOI: 10.1155/2021/5589285
  17. Martins, G. B., Papa, J. P., & Adeli, H. (2020). Deep learning techniques for recommender systems based on collaborative filtering. Expert Systems, 37(6), e12647. DOI: 10.1111/exsy.12647
  18. Jiang, X. (2024). Multi-Layer Self-Attentive Sequential Recommendation. In 2024 9th International Conference on Intelligent Computing and Signal Processing (ICSP) (pp. 1205-1210). IEEE. DOI: 10.1109/ICSP62122.2024.10743339
  19. Sweller, J. (2020). Cognitive load theory and educational technology. Educational Technology Research and Development, 68(1), 1-16. DOI: 10.1007/s11423-019-09701-3
  20. Ma, T. M., Wang, X., Zhou, F. C., & Wang, S. (2023). Research on diversity and accuracy of the recommendation system based on multi-objective optimization. Neural Computing and Applications, 35(7), 5155-5163. DOI: 10.1007/s00521-020-05438-w
  21. Jannach, D., & Abdollahpouri, H. (2023). A survey on multi-objective recommender systems. Frontiers in Big Data, 6, 1157899. DOI: 10.3389/fdata.2023.1157899
  22. Ma, H., Zhang, Y., Sun, S., Liu, T., & Shan, Y. (2023). A comprehensive survey on NSGA-II for multi-objective optimization and applications. Artificial Intelligence Review, 56(12), 15217-15270. DOI: 10.1007/s10462-023-10526-z
  23. Verma, S., Pant, M., & Snasel, V. (2021). A comprehensive review on NSGA-II for multi-objective combinatorial optimization problems. IEEE Access, 9, 57757-57791. DOI: 10.1109/ACCESS.2021.3070634
  24. Zhong, S. (2025). Research on Multi-Objective Optimization Recommendation Algorithms for Work-Study Platforms. Journal of Computer, Signal, and System Research, 2(1), 168-180. DOI: 10.71222/bwj2jw48
  25. Li, M., Fu, Q., Singh, V. P., Liu, D., Li, T., & Zhou, Y. (2020). Managing agricultural water and land resources with tradeoff between economic, environmental, and social considerations: A multi-objective non-linear optimization model under uncertainty. Agricultural Systems, 178, 102685. DOI: 10.1016/j.agsy.2019.102685
  26. Liao, W., & Fan, C. (2025). A Multimodal-Based Fingering Analysis Method for Piano Playing. IEEE Access, 13, 205288-205295. DOI: 10.1109/ACCESS.2025.3639710
  27. Shrivastava, R., Sisodia, D. S., & Nagwani, N. K. (2022). Utility optimization-based multi-stakeholder personalized recommendation system. Data Technologies and Applications, 56(5), 782-805. DOI: 10.1108/DTA-07-2021-0182
  28. Zou, F., Chen, D., Xu, Q., Jiang, Z., & Kang, J. (2021). A two-stage personalized recommendation based on multi-objective teaching–learning-based optimization with decomposition. Neurocomputing, 452, 716-727. DOI: 10.1016/j.neucom.2020.08.080
  29. Wu, H., Ma, C., Mitra, B., Diaz, F., & Liu, X. (2022). A multi-objective optimization framework for multi-stakeholder fairness-aware recommendation. ACM Transactions on Information Systems, 41(2), 1-29. DOI: 10.1145/3564285
  30. Cai, X., Hu, Z., & Chen, J. (2020). A many-objective optimization recommendation algorithm based on knowledge mining. Information Sciences, 537, 148-161. DOI: 10.1016/j.ins.2020.05.067
  31. Zaizi, F. E., Qassimi, S., & Rakrak, S. (2025). Multi-objective Optimization for Personalized and Fairness Recommender Systems: A Hybrid Approach. SN Computer Science, 6(6), 713. DOI: 10.1007/s42979-025-04230-8
  32. Zaizi, F. E., Qassimi, S., & Rakrak, S. (2025). A deep autoencoder-enhanced multi-objective evolutionary algorithm for recommender systems. The Journal of Supercomputing, 81(10), 1116. DOI: 10.1007/s11227-025-07589-w
  33. Zhou, W., Liu, Y., Li, M., Wang, Y., Shen, Z., Feng, L., & Zhu, Z. (2023). Dynamic multi-objective optimization framework with interactive evolution for sequential recommendation. IEEE Transactions on Emerging Topics in Computational Intelligence, 7(4), 1228-1241. DOI: 10.1109/TETCI.2023.3251352

© 2026 The Author(s). Published by Sichuan Knowledgeable Intelligent Sciences. This is an open access article under the CC BY 4.0 license.

How to cite: Feng, C. (2026). Personalized Recommendation Algorithm of Piano Practice Scheme Based on Multi Objective Optimization. International Scientific Technical and Economic Research, 4(1), 245-265. https://doi.org/10.71451/ISTAER2612