# Research on Learning Resource Recommendation Based on Knowledge Graph and Collaborative Filtering
## 1 Introduction
A learning object recommendation model based on heterogeneous learning behavior and knowledge graph is proposed, which eliminates the influence of noise and obtains complex interaction behavior by using an attentive composition-based graph convolutional network (ACGCN) and dense feature-based operation-aware network (DFOAN) [20]. In conventional recommendation systems, the common practice is to utilize explicit rating data provided by users [21]. However, in the realm of online course learning, explicit rating actions are infrequent, rendering the application of explicit feedback to personalized learning resource recommendations challenging. In contrast, implicit feedback, such as user interactions like clicks and browsing behavior on items [22], can be effectively captured and analyzed. Implicit feedback can customize recommendations based on each interaction [23]. In their course activities, students leave behind traces that implicitly express their preferences. This type of data is relatively easy to gather and can be leveraged as implicit feedback. To explore the utilization of implicit data in recommendation systems, researchers have delved into various dimensions. [PERSON] et al. [24] concentrated on implicit content within multimedia interactions and introduced a novel attention mechanism called Attentive Collaborative Filtering (ACF) into collaborative filtering algorithms to incorporate user behavior information and preferences. [PERSON] et al. [25] underscored the significance of user micro-behavior, which offers a deeper and more granular understanding of users. They put forth a recommendation framework for modeling the sequence and impact of micro-behavior, abbreviated as RIB. Meanwhile, [PERSON] et al. [26] posited that learners' learning behavior can provide deeper insights into their knowledge mastery. They introduced a learning behavior-oriented knowledge tracing (LBKT) model and investigated three typical learning behaviors: speed, attempts, and hints. By analyzing these learning behaviors, it becomes possible to infer the most indicative knowledge state. While knowledge graph-based recommendation systems excel in comprehension, they exhibit limited adaptability to the personalized needs of learners. On the other hand, collaborative filtering-based recommendation systems can provide personalized learning suggestions, but they often encounter data sparsity and cold start issues, making it difficult to obtain the best recommendations [27; 28]. Researchers have uncovered that the integration of knowledge graphs into recommendation systems can enhance recommendation performance, augment interpretability, and bolster personalization [29]. [PERSON] et al. [30]amalgamated the RippleNet model with collaborative filtering, taking into account both movie attribute data and user-movie interaction data. This approach seeks to strike a balance between considering item attributes and user interactions. In a similar vein, [PERSON] et al. [31], building upon collaborative filtering, crafted item knowledge graphs and user knowledge graphs to enhance information richness. This involved enriching the matrix with item feature data and extracting user preferences to enhance recommendation accuracy. [PERSON] et al. [32] integrated the knowledge graph into the task of ocean forecasting data while considering the correlation of user behavior among forecasters. Building upon the aforementioned background, this article integrates knowledge graph information into the recommendation system by constructing a knowledge graph of the course on the basis of collaborative filtering recommendations. In addition, construct an implicit feedback model, mine students' implicit feedback data, and design different preference rules to apply to the recommendation process. The primary contributions of this article are outlined as follows:  
1. This study integrates user learning behavior with the semantic information of learning resources, leveraging the strengths of both knowledge graphs and collaborative filtering algorithms. 2.