The COVID-19 pandemic has catalyzed a substantial increase in the availability of online academic resources. Users and students frequently encounter challenges in identifying the most pertinent content amidst this vast information space. Consequently, recommendation systems have become indispensable for curating content by leveraging data from prior interactions and resource characteristics. While significant research has been conducted in this domain, existing methodologies are often constrained to specific information types, resulting in the underutilization of diverse data sources. In contrast, fields such as entertainment and e-commerce have successfully employed deep learning models to integrate various data forms, including videos, user-item interactions, and thematic content. However, the application of these advanced techniques has yet to be explored in academic contexts. This study explores the integration of unstructured data, such as descriptions, and structured data, such as knowledge graphs, in the context of academic recommendation systems. We utilize two distinct datasets: MOOCCubeX, an open MOOC dataset, and DBLPv12, a citation network dataset, to evaluate the impact of diverse information sources on recommendation performance. Our findings indicate that deep learning models incorporating heterogeneous data types significantly enhance the accuracy and relevance of recommendations, thereby demonstrating the potential of these approaches in academic resource recommendation.