大型开放式网络课程资源个性化推荐算法

Design of Personalized Recommendation Algorithm for Large Open Online Course Resources

为了提高网络课程资源个性化推荐的准确性,提出一种大型开放式网络课程资源个性化推荐算法。首先对相关的网络课程资源进行了聚类处理,然后生成了网络课程资源个性化推荐的候选队列,通过分列和集合操作,得到行为高度与用户本身的基本信息重合得到队列长度。最后采用TF-IDF方法实现了大型开放式网络课程资源个性化推荐。实验结果表明,所研究的推荐算法较传统算法推荐准确性高,并且减少了推荐时间,满足推荐算法设计需求。

In order to improve the accuracy of personalized recommendation of online course resources,a personalized recommendation algorithm of large-scale open online course resources is proposed and designed.Firstly,the network course resources are clustered,and then the candidate queue for personalized recommendation of network course resources is generated.Through the operation of column and set,the behavior height is obtained to overlap with the basic information of users and the queue length is obtained.Finally,TF-IDF method is used to realize the personalized recommendation of MOOC resources.The experimental results show that the proposed algorithm has higher accuracy than the traditional algorithm,and reduces the recommendation time,which meets the design requirements of the recommendation algorithm.