国家自然科学基金(61972300, U21B2015, 62202356); 陕西省科协青年人才托举计划(20220113); 西安电子科技大学智慧金融软件工程新技术联合实验室项目(99901220858)
随着互联网信息技术的高速发展, 线上学习资源的爆炸式增长引起了“信息过载”与“学习迷航”问题. 在缺乏专家指导的场景中, 用户难以明确自己的学习需求并从海量的学习资源中选择合适的内容进行学习. 教育领域推荐方法能够基于用户的历史学习行为提供学习资源的个性化推荐, 因此该方法近年来受到大量研究人员的广泛关注. 然而, 现有的教育领域推荐方法在学习需求感知时忽略了对知识点之间复杂关系的建模, 同时缺乏考虑用户学习需求的动态性变化, 导致推荐的学习资源不够精准. 针对上述问题, 提出一种基于静态与动态学习需求感知的知识点推荐方法, 通过静态感知与动态感知相结合的方式建模复杂知识关联下的用户学习行为. 对于静态学习需求感知, 创新性地设计一种基于知识点先修后继元路径引导的注意力图卷积网络, 通过建模知识点之间先修后继关系的复杂约束, 能够消除其他非学习需求因素的干扰, 从而精准地捕获用户在细粒度知识点层面上的静态学习需求; 对于动态学习需求感知, 所提方法以课程为单元聚合知识点嵌入以表征用户在不同时刻的知识水平, 然后采用循环神经网络建模编码用户的知识水平序列, 能够有效地挖掘用户知识水平变化中蕴含的动态学习需求; 最后, 对获得的静态与动态学习需求进行融合, 在同一框架下建模静态与动态学习需求之间的兼容性, 促进这两种学习需求相互补充, 以实现细粒度的个性化知识点推荐. 实验表明, 在两个公开数据集上, 所提方法能够有效地感知用户的学习需求并提供个性化的知识点推荐, 在多种评估指标上优于主流的推荐方法.
With the rapid development of Internet information technologies, the explosive growth of online learning resources has caused the problem of “information overload” and “learning disorientation”. In the absence of expert guidance, it is difficult for users to identify their learning demands and select the appropriate content from the vast amount of learning resources. Educational domain recommendation methods have received a lot of attention from researchers in recent years because they can provide personalized recommendations of learning resources based on the historical learning behaviors of users. However, the existing educational domain recommendation methods ignore the modeling of complex relationships among knowledge points in learning demand perception and fail to consider the dynamic changes of users’ learning demands, which leads to inaccurate learning resource recommendations. To address the above problems, this study proposes a knowledge point recommendation method based on static and dynamic learning demand perception, which models users’ learning behaviors under complex knowledge association by combining static perception and dynamic perception. For static learning demand perception, this study innovatively designs an attentional graph convolutional network based on the first-course-following meta-path guidance of knowledge points, which can accurately capture users’ static learning demands at the fine-grained knowledge point level by modeling the complex constraints of the first-course-following relationship between knowledge points and eliminating the interference of other non-learning demand factors. For dynamic learning demand perception, the method aggregates knowledge point embeddings to characterize users’ knowledge levels at different moments by taking courses as units and then uses a recurrent neural network to encode users’ knowledge level sequences, which can effectively explore the dynamic learning demands hidden in users’ knowledge level changes. Finally, this study fuses the obtained static and dynamic learning demands, models the compatibility between static and dynamic learning demands in the same framework, and promotes the complementarity of these two learning demands to achieve fine-grained and personalized knowledge point recommendations. Experiments show that the proposed method can effectively perceive users’ learning demands, provide personalized knowledge point recommendations on two publicly available datasets, and outperform the mainstream recommendation methods in terms of various evaluation metrics.