The Integration and Innovation of Sports Social Platforms and Information Technology

Abstract

In order to better achieve the integration and innovation of sports social platforms and information technology, the author proposes an SFD (Sport Friend Discover) sports friend recommendation model based on physical testing big data. The core idea of this model is to use physical measurement data to match the similarity between athletes and recommend suitable exercise partners. Specifically, we collected a large amount of physical measurement data, including height, weight, body fat percentage, muscle mass, etc. Then, through data mining algorithms, these data are transformed into feature vectors of the movers. Next, we use a similarity algorithm to calculate the similarity between different athletes and find the most matching motion partner with the user. The results show that the SFD method outperforms the other two traditional recommendation methods on the dataset, with P @ 10, P @ 20, P @ 30, and P @ 40 of SFD reaching 0.099, 0.095, 0.085, and 0.591, respectively. SFD not only utilizes more neighboring information than FOAF based on local graph structure, but also compared to TRW based on global graph structure method, at the same time, the importance of the node itself is also considered, resulting in higher accuracy. It has been proven that the SFD sports friend recommendation model based on physical testing big data has achieved good results in recommending sports partners. Users can quickly find sports partners with similar body types and health conditions, improving the fun and effectiveness of exercise.