Personalized learning resource recommendation is an essential component of intelligent tutoring systems. To address the issue of the plethora of learning resources and enhance the learner experience in intelligent tutoring systems, learning resource recommendation systems have been developed to model learners’ preferences. Despite numerous efforts and achievements in academia and industry toward more personalized learning, intelligent education tailored to individual learners still faces challenges, such as inadequate user representation and potential information loss during the aggregation of multi-source heterogeneous information features. In recent years, knowledge-graph-based recommendation systems have brought hope for mitigating these issues and achieving more accurate recommendations. In this paper, we propose a novel personalized learning resource recommendation method based on a knowledge graph named the Learner-Enhanced Knowledge Graph Attention (LKGA) network. This model enhances learner representation by extracting collaborative signals, where learning resources clicked by learners who have clicked the same resource are considered potential collaborative signals and are concatenated with the original learning resource features to form the initial entity set for the learner. Furthermore, during the entity aggregation process, each tail entity has different semantic expressions, and an attention mechanism is used to distinguish the importance of different neighbor entities. Additionally, residual connections are added in each hop of the learner’s aggregation process, with the information from the first hop added to each subsequent hop to reduce information loss. We applied the proposed LKGA model to a real-world dataset, and the experimental results fully validate the effectiveness of our model.

Item description information plays a crucial role in helping users understand the basic situation of an item and is also vital auxiliary information in recommendation systems. Traditional methods obtain this data through platform backend data or web scraping techniques, but these data are often static, relatively fixed, and insufficiently descriptive. In recent years, large language models (LLMs) like generative pre-trained transformer (GPT) have become powerful tools in natural language processing, bringing new hope for LLM-based recommendations. However, does the text information generated by large language models help improve recommendation accuracy? How can the information produced by generative artificial intelligence be integrated with existing multi-source heterogeneous information? In this paper, we propose a novel deep hybrid recommendation method for multimodal information integrating content generated by large language models (DML). We first explore the use of large language models to generate detailed descriptive information about movies. Next, we perform a weighted fusion of the generated text information with existing movie category information and user demographic data, among other multi-source heterogeneous information. Finally, we use the fused information to predict movie ratings. The results indicate that the multimodal information deep hybrid recommendation method, which integrates content generated by large language models, provides substantial evidence of superior performance relative to existing baseline models.