Personalized Health Plan Recommendation Algorithm Based on User Data of Network Sports Software

As an emerging fitness method, online sports software has been favored by a large number of users, but it cannot record user exercise data, which affects the execution of fitness plans by users. A personalized health plan recommendation algorithm based on user data of network sports software was studied to address this issue. The algorithm used deep learning technology to train user sports data and achieve personalized fitness plan recommendation. The experimental results showed that the final mean absolute error of the algorithm has been reduced to around 0.02, which not only effectively improves the recommendation performance of the recommendation algorithm, but also helps users effectively execute health plans.