# Enhancing the Recommendation of Learning Resources for Learners via an Advanced Knowledge Graph
## 1 Introduction
[12] proposed the Knowledge Group Aggregation Network (KGAN) model, which naturally projects learner behavior and course graphs into a unified space, effectively alleviating challenges such as interaction sparsity, course relevance, and intention diversity in course recommendation. [PERSON] et al. [13] introduced the MC-DRER exercise recommendation model for online problem-solving, which constructs a knowledge graph in the field of programming and establishes associations between exercises and their knowledge points, enabling the recommendation method to handle relationships between knowledge points. Although knowledge-graph-based recommendation systems have led to some progress in personalized learning, there are still two urgent issues in the field of personalized learning resource recommendation: (1) A significant amount of research has focused on the representation of learning resources but neglected the representation of learners. (2) The aggregation strategies for multi-source heterogeneous information are relatively simplistic, which can easily lead to information loss and the failure to distinguish the importance of different neighboring entities. We assume that in a certain intelligent education recommendation scenario, there is a set of M learners and a set of N learning resources. Based on the implicit feedback of learners in historical records, we can obtain a learner-resource interaction matrix, where \(\mathrm{r_{ij}}\) indicates that learner \(\mathrm{i}\) has interacted with learning resource \(\mathrm{j}\), and otherwise, \(\mathrm{r_{ij}}=0\). Additionally, we organize auxiliary information into the form of a knowledge graph, where each knowledge triple (h,r,t) represents a relationship between the head entity h and the tail entity t, and E and R are the sets of entities and relationships in the knowledge graph, respectively. Our objective is to learn a predictive function. This paper proposes a learner-enhanced personalized learning resource recommendation model (LKGA) to effectively mitigate the challenges posed by the aforementioned issues. This specific work includes three aspects: First, by extracting collaborative signals from users, the learning resources clicked by learners who have clicked the same resource are considered potential collaborative signals and merged with the original learning resource features to form the initial entity set for learners. Explicitly encoding the collaborative information in potential learner-learning resource interactions naturally integrates these signals with knowledge propagation. Second, during the entity aggregation process, an attention mechanism is employed to distinguish the different semantic expressions of each tail entity, thereby treating the contributions of different neighbors differently. Third, initial connections are added during each hop of the learner's aggregation process, and including the information from the first hop in every subsequent hop can effectively reduce information loss, thereby further enhancing the representation capability of learners. In summary, our contributions include the following:  
1. We propose a novel, end-to-end, personalized learning resource recommendation model, LKGA, which more comprehensively represents the original entity sets of learners and learning resources by simultaneously considering user collaborative signals and learning resource collaborative signals. The model facilitates embedding representations by naturally combining collaborative propagation between learners and learning resources with knowledge graph propagation. 2. Furthermore, the model effectively distinguishes the semantic differences of tail entities by fully leveraging the attention mechanism and reduces information loss by adding initial connections during the aggregation process. This helps to focus on the important roles of key neighbors and further enhances the effective representation capability of learners, allowing for deeper expression of the latent meanings of learners in the vector space. 3.