可解释个性化推荐学习平台的构建与算法研究

Construction and algorithm research of interpretable personalized recommendation learning platform

目前在线学习平台的个性化推荐功能过于注重推荐效果的准确性、多样性和新颖性,忽视了学生的用户体验等问题,为此,提出构建可解释个性化推荐在线学习平台。首先,对平台的系统框架进行设计,详细研究了实现算法,并对可解释个性化推荐功能的核心算法及形成可解释性语句的推荐流程进行了重点阐述。然后,利用多种推荐算法混合计算的方式对学生进行课程的个性化推荐,并根据对应特征生成解释语句以表明推荐理由。其结果是能有效提高学生对推荐课程的认可度和学习效率,改善平台的个性化推荐效果和用户体验,从而提高了平台的可信度和透明度。

In view of the fact that the personalized recommendation function of the current online learning platform cares too much about accuracy,diversity and novelty of the recommendation effect,while ignoring deficiency of the user experience and other problems,this paper proposed construction of the interpretable personalized recommendation online learning platform by designing the system framework of the whole platform and probing into the implementation algorithm in detail.It emphatically focused on the core algorithm of the interpretable personalized recommendation function and the recommendation process of the interpretable statement.The personalized recommendation of courses was tailored to users by means of mixed calculation of various recommendation algorithms,with reasons for recommending courses available to users by generating interpretable statements according to the corresponding characteristics.It can effectively improve users recognition of recommended courses and learners learning efficiency,enhance the personalized recommendation effect and user experience of the platform,and increase the credibility and transparency of the platform.