Many precise personalized learning recommendations in massive open online courses (MOOCs) have emerged in the intelligence education field. Up to now, most researches simply put the dual learner-resources relations into consideration and are short of studies looking deep into its intrinsic social relation, thus rarely introducing the influential factors such as social trust, which means to apply the mutual trust relation between learners in the precise personalized learning recommendation. Therefore, we propose a personalized learning recommendation method based on learners’ trust and conduct a quantitative analysis on two aspects: social trust and influence, so as to realize a precise personalized learning recommendation service. First, we establish a new module on social trust scale which integrates the interactive information and preference degree to reveal the implicit trust relation between learners in social networks and construct social trust networks. Next, we adopt improved structural hole (ISH) algorithm by integrating the topological structure of social trust network with learners’ interactive information and identify the most influential learners cluster by the ISH algorithm. For the final stage, we predict the score of target learners based on explicit and implicit feedback information and realize the personalized learning recommendation for new learners. Since the score is predicted, we compare MAE and RMSE in two real-world datasets which are Canvas Network and Wanke website, respectively. The result of experiment validates the accuracy and effectiveness of our recommendation model.

Graph neural network is blossoming recently,and it can explicitly express user-item highorder connectivity information,so it can significantly improve the recommendation performance when it applied to recommender system. However, the existing methods usually assume that the user's interest is invariant,and there is insufficient explore to charactertize the user's dynamic interest changes through the temporal sequences dependencies of items. In this paper, we propose a research on graph neural network collaborative recommendation model fused item temporal sequence relation, that is, a top-N hybrid recommendation model that fuses user-item interaction information and item temporal sequences dependencies. It divides the item temporal sequences into several groups of subsequences through the sliding window, constructs the item temporal sequences dependency graph, aggregates the characteristics of item temporal sequences information, and deeply depict the dynamic changes of users' interests, and uses the bipartite graph neural network to map the high-dimensional information of user-item and item-item to the low-dimensional space. The hybrid embedding of user-item historical interaction information and item temporal sequences dependency information is realized, and the expression of user-item interaction sequence information is enhanced. Finally, through the user-item interaction graph and item sequence dependency graph, constructed a graph neural collaborative filtering recommendation framework including embedding layer,aggregation layer,propagation layer and prediction layer. Experiments demonstrate that the model performance has been significantly improved on datasets such as LastFM, Ciao and Douban.