Abstract:

For the monotonicity of learning resources recommended by the collaborative filtering algorithms may lead to the difficulty of meeting the need of personalized resource acquisition of learners， a recommendation algorithm for online learning resources based on double-end neighbor fusion knowledge graph was proposed. Firstly， on the user end， the information of the entities and their neighbors between the learner’s existing knowledge nodes and the new knowledge nodes were aggregated to obtain the embedding representation of the learner in order to capture the learner’s personalized requirements. Secondly， on the project end， the neighborhood information of learning resources was used to expand the semantics and embedding representation of the learning resources. Finally， the user embedding representation and the project embedding representation were sent to the fully connected layer to obtain the interaction probability of them. To verify the effectiveness of the proposed algorithm， comparison experiments were performed using the public dataset MOOPer. Experimental results show that on this dataset， the proposed algorithm improves 1.12 percentage points and 1.31 percentage points on AUC （Area Under Curve） and accuracy respectively compared to the optimal baseline model， and achieves certain improvement on both of Precision@K and Recall@K.

Key words: knowledge graph, online learning, resource recommendation, Multi-Layer Perceptron (MLP), feature propagation

摘要：

针对协同过滤算法推荐学习资源的单一性弱点导致的学习者的个性化资源获取需求难以满足的问题，提出融合知识图谱邻居双端的在线学习资源推荐算法。首先，在用户端将学习者的既有知识节点与新知识节点之间的实体及其邻居信息聚合得到学习者的嵌入表示，从而捕捉学习者的个性化需求；其次，在项目端利用学习资源的邻域信息扩充学习资源的语义与嵌入表示；最后，将用户的嵌入表示和项目的嵌入表示送入全连接层以得到二者的交互概率。为了验证所提算法的有效性，使用公开数据集MOOPer进行对比实验。实验结果表明，在该数据集上，所提算法相较于最优基线模型在曲线下面积（AUC）和准确率上分别提升了1.12个百分点和1.31个百分点，且在Precision@K和Recall@K上均有一定的提高。

关键词: 知识图谱, 在线学习, 资源推荐, 多层感知器, 特征传播