Sports training analysis method based on collaborative filtering

Abstract

With the continuous development of information technology, data mining methods are gradually accepted and recognized by people. The use of data mining methods can realize the sorting and exploration of data laws. Collaborative filtering technology, as a more important recommendation algorithm in data mining, is the best method for data mining. Discovery provides convenience. However, the current collaborative filtering recommendation algorithm has low operating efficiency and low recommendation accuracy in data mining. Aiming at this problem, an improved collaborative filtering recommendation algorithm is established on the basis of collecting behavior characteristics. First, analyze the application of collaborative filtering algorithm in data mining, and analyze the shortcomings of collaborative filtering algorithm; second, optimize the overall process of collaborative filtering algorithm in data mining; finally, take sports athlete recommendation items as an example to verify the improved The difference between the algorithm and the traditional algorithm in terms of time efficiency and accuracy. The experimental results show that the collaborative filtering recommendation algorithm based on data mining established in this study can reduce the running time of the algorithm, enhance the accuracy of the algorithm, and has strong practicability.