The use of the course recommendation systems is an important focus of research in the field of educational technology. Understanding how students interact with them and accept these systems is essential as the learning environment is changing due to the integration of digital platforms. The Recommendation systems (RSs) are useful tools for narrowing down the course options and exposing students to the courses that suit their needs. The majority of the research related to recommendation systems focuses on effectiveness rather than factors influencing its acceptability, and in practice, user satisfaction cannot be explained by accuracy alone. This study considers the course recommendation systems and examines whether the courses proposed by our recommender systems (RS) are accepted by learners, particularly research students, based on their learning requirements. This can help researchers understand why some users embrace new technology while others resist it. Therefore, research scholars (n=150) willingly engaged in this study were asked to use the RS and complete a questionnaire based on their experience as part of a self-administrated longitudinal survey. This study evaluates the effect of external variables that the Technology Acceptance Model (TAM) does not account for, such as perceived availability, relevance, and experience. It also evaluates the recommendation system’s capacity for making accurate recommendations. When compared to our keyword (75.11% accuracy) and N-gram (89.85%) based approaches, the accuracy of our hybrid recommendation was calculated to be 95.25 percent. The findings further support the extended TAM’s role as a useful theoretical framework for explaining academics’ acceptance of RS and other elements that have a positive bearing on the TAM’s core variables. Consequently, a new modified TAM that includes three outside elements is proposed. The results’ validity and dependability are confirmed by the significant value calculated for Cronbach’s alpha. Because the ramifications of this study effort are crucial for faculties, scholars, and institutions, the observed results can help developers of the recommendation systems in maximizing the user experience.