# Recommender System for University Degree Selection:
## 2 Related Work
### Application of Recommender Systems in Educational Settings  
In recent years, recommendation systems in the educational field have gained significant popularity, encompassing various stages of the student's relationship with academic institutions. From a high-level perspective, there are systems designed for university enrolment recommendations. These educational recommender systems aim to enhance the relevance of proposed study programmes to students' interests. Specifically, their objectives include mapping students' interests to a broad range of available courses to ensure they find courses that match their learning needs [18]. With recent developments in the availability of online content, there is a growing need for systems that perform a data analysis to find the best match for students based on their unique interests and capabilities [19]. Researchers have conducted numerous studies to develop systems that fill this gap using intelligent information processing techniques, such as recommender systems. Throughout the literature, various approaches leverage recommender system techniques, including collaborative filtering [20], content-based filtering, and hybrid recommender systems based on fuzzy systems [21] and complex machine learning models [22]. Each approach collects and analyses students' historical behaviours, interests, or performance measurements to provide personalised recommendations. For example, collaborative filtering assumes that measuring the degree of similarity between users' experiences can help identify items of interest. Suppose two students tend to enrol in similar courses. In that case, it is likely that if student A receives a good grade in a particular course, student B will also perform well, as they have demonstrated similar academic behaviours in the academic records [10]. However, these methods often fall short in contexts where student capabilities, learning curves, and backgrounds are highly diverse, leading to issues like data sparsity and limited generalizability in recommendations. Conversely, content-based approaches incorporate textual data from users' previous histories. Unlike collaborative filtering, content-based systems do not suffer from issues related to data matrix sparsity. However, these approaches require mechanisms to transform text features such as tags, keywords, and topics into vectors [23]. For instance, a content-based recommender system effectively assists prospective students in Bangladesh choose top-K private universities based on several parameters, achieving high accuracy [12]. After creating vector representations, content-based approaches use cosine similarity to determine the degree of similarity between users. However, these systems often neglect critical external influences on a student's educational journey, such as family income, parental occupation, and other socioeconomic factors. Moreover, hybrid frameworks combine complex models to generate personalised recommendations. Machine learning applications incorporating deep neural networks have produced robust and accurate recommendations. Natural language processing techniques, such as latent Dirichlet allocation (LDA) and latent semantic analysis (LSA), are also used to process extensive textual profiles to understand the semantics of the information used in the recommendation process [24]. Despite their advantages, textual features often contain inherent biases and uncertainties. Researchers have widely adopted fuzzy logic systemsto handle these issues, which model vagueness logically and provide heuristic rules [25]. This approach supports the present research's development by incorporating a student's perspective through text analysis. In educational recommender systems, fuzzy logic consistently mitigates vagueness by using linguistic variables and can be combined with other approaches, such as clustering, to find concrete user similarities. For instance, fuzzy rules can better interpret user ratings on specific items, offering greater flexibility in handling demographic data [26].