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

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.