Recommender systems provide personalized suggestions by processing user and item information and interactions. Personalized product recommendations make it easier for users to access products that interest them. Course recommendation systems, on the other hand, aim to guide students to fields of interest in which they can succeed. On e-learning sites, there are many courses and students from different fields. Also, students can select courses from other than the fields they are studying. However, students in educational institutions must follow a curriculum. Since each educational institution has distinct constraints on course selection, a specific approach to the problem is required to develop a course recommender system. Due to the restrictive nature of the problem, developing a recommendation system for institutions is considered challenging. Therefore, students consult a faculty member when selecting a course for enrollment. In this study, a hybrid recommender system is proposed using student and course information with collaborative filtering and content-based filtering models. The proposed system provides consistent recommendations by using explicit and implicit data, without predefined association rules. The collaborative filtering algorithms use grades as rating values. The content-based filtering algorithms utilize text-based information about students and courses by converting them into feature vectors using natural language processing methods. In the combination phase of the hybrid recommender system, only one of the collaborative filtering and one of the content-based filtering models are used with different ensembling methods. It is found that the suggested hybrid recommender system can achieve outperforming results for all evaluation metrics. The results show the values of the rank-aware metrics Precision@N, AP@N, mAP@N, and NDCG@N for the individual models and the hybrid models with different combinations. In particular, for content-based filtering with Bayesian personalized ranking, the hybrid model performs better than any algorithm in practice.

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Medical doctors may struggle to diagnose dementia, particularly when clinical test scores are missing or incorrect. In case of any doubts, both morphometrics and demographics are crucial when examining dementia in medicine. This study aims to impute and verify clinical test scores with brain MRI analysis and additional demographics, thereby proposing a decision support system that improves diagnosis and prognosis in an easy-to-understand manner. Therefore, we impute the missing clinical test score values by unsupervised dementia-related user-based collaborative filtering to minimize errors. By analyzing succession rates, we propose a reliability scale that can be utilized for the consistency of existing clinical test scores. The complete base of 816 ADNI1-screening samples was processed, and a hybrid set of 603 features was handled. Moreover, the detailed parameters in use, such as the best neighborhood and input features were evaluated for further comparative analysis. Overall, certain collaborative filtering configurations outperformed alternative state-of-the-art imputation techniques. The imputation system and reliability scale based on the proposed methodology are promising for supporting the clinical tests.