基于元路径和注意力机制特征融合的知识概念推荐

Knowledge Concept Recommendation Based on Meta-path and Attentional Feature Fusion

在课程推荐方面的研究领域中绝大多数研究都是针对课程或视频进行推荐,极少有研究关注到对特定知识概念的兴趣或需求。现有的工作主要集中在同构图上,普遍存在用户-项目关系稀疏性的问题。为缓解稀疏性问题,充分利用MOOC数据集拥有的具有多元实体及富含语义信息的上下文关系的特点,本文提出一种结合图卷积神经网路的基于元路径和注意力机制特征融合的知识概念推荐算法(MAFRec)。首先,提取各实体的内容特征和实体间的上下文特征,将其建模为异构信息网络并提取元路径,将元路径对应的邻接矩阵输入到图卷积神经网络中,并在融合了元路径特征向量和用户、概念潜在特征向量的双层网络结构的注意力机制的指导下学习用户和概念实体的表示,最终将用户和概念实体的表示合并到一个扩展的矩阵分解框架中,以预测每个用户的概念兴趣得分。在MOOCCube数据集上的实验结果表明,该算法较BPR、FISM、NAIS、Metapath2vec和MOOCIR算法在命中率、归一化折损累计增益和平均倒数排名指标上均为最优,在一定程度上提高了推荐过程的预测精度和可解释性,并缓解了用户-概念稀疏性的问题。

In the research of course recommendation,the most of research effort was focused on course or video resource recommendation,only few studies paid attention to the interest or need of users for specific knowledge concept.Existing researches focus primarily on homogeneous graphs,are vulnerable to the problems of user-item relationships sparsity.To copy with the sparsity problem and fully utilize the characteristics of MOOCs datasets with multiple entities and a lot of semantic information in context relationships,a knowledge concept recommendation algorithm based on meta-path and attentional feature fusion was proposed.First,we extracted the content features of each entity and the context features between entities,input the adjacency matrices based on selected meta-paths into the graph convolutional network,and learned the representation of users and concepts under the guidance of the attention mechanism of the two-layer network structure that integrated the feature vectors of the metapath and potential feature vectors of users and concepts.Finally,these learned user and concept representations were incorporated into an extended matrix factorization framework to predict the preference of concepts for each user.Experimental results on MOOCCube dataset demonstrate that the algorithm attains the best hit rate,the best normalized discounted cumulative gain and the best mean reciprocal ranking than those of BPR,FISM,NAIS,Metapath2vec,and MOOCIR algorithms.The algorithm improves the interpretability and prediction accuracy of the recommendation process to a certain extent,and alleviates the problem of user-item relationships sparisty.