远程培训中个性化学习资源推荐算法

Recommendation Algorithm of Personalized Learning Resources in Remote Training

本文提出一种基于标签的多因素推荐算法.用户可以根据自己的需求,进行因素自定义和优先级排序,算法先根据用户初始化信息选取资源,随后分析用户行为数据更新用户所属的群及用户的喜好,再通过用户与项目相似度计算、项目关联度计算为用户推荐所需资源.算法模型采用分类组合得出结果,降低了相似度计算的复杂度.将算法应用于企业远程培训平台的个性化学习模式中,结果表明,该算法较好地改善了用户个性化学习资源的推荐效果.

This paper puts forward a recommendation algorithm of various factors based on the labels. Users can define the factors and sort them with priority according to their own requirements., This algorithm will select the resources according to the initial information of users, then update users＇ groups and users＇ preferences, then recommend the useful resources for users based on the similarity calculation between users and project and the correlation calculation of the project. The algorithm model adopts classification of combination to get the results, and reduces the complexity of similarity calculation. The algorithm was applied to the personalized learning platform of remote training platform in enterprise. The results show that this algorithm has much improved the recommendation effects of user＇s personalized learning resources.