基于协同过滤的体育运动员适宜项目推荐方法研究

Research on the method of sports athlete’s suitable item recommendation based on collaborative filtering technology

当今社会信息化技术快速发展,体育运动领域也逐渐运用计算机手段解决面临的问题。体育运动员由于所选项目与自身特点不匹配,导致运动员的积极性下降。针对此问题,提出基于协同过滤技术的体育运动员适宜项目推荐模型,分析传统协同过滤方法的缺陷和局限性,对传统的协同过滤算法进行改进,对运动员兴趣程度及项目属性评分进行加权推荐,并利用体育运动员的实际数据进行模型分析验证。通过实验可知,改进后的协同过滤技术在体育运动员项目推荐上具有良好的准确率和效率。

With the rapid development of information technology in today’s society,the field of sports is gradually using computer means to solve the problems.Due to mismatch between the selected items and their own characteristics,the athletes’enthusiasm decreases.In response to this problem,a suitable item recommendation model for sports athletes based on collaborative filtering technology is proposed,which analyzes the defects and limitations of traditional collaborative filtering methods,and improves the traditional collaborative filtering algorithm.Besides,it also weighted recommend the athlete’s interest level and item attribute scores,and use the actual data of sports players for model analysis and verification.The experiment shows that the improved collaborative filtering technology has good accuracy and efficiency in the recommendation of sports athletes.