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5,648 views
9 citations
Original Research
02 February 2022
Developing and Evaluating a University Recommender System
Mehdi Elahi
3 more and 
Christoph Trattner
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A challenge for many young adults is to find the right institution to follow higher education. Global university rankings are a commonly used, but inefficient tool, for they do not consider a person's preferences and needs. For example, some persons pursue prestige in their higher education, while others prefer proximity. This paper develops and evaluates a university recommender system, eliciting user preferences as ratings to build predictive models and to generate personalized university ranking lists. In Study 1, we performed offline evaluation on a rating dataset to determine which recommender approaches had the highest predictive value. In Study 2, we selected three algorithms to produce different university recommendation lists in our online tool, asking our users to compare and evaluate them in terms of different metrics (Accuracy, Diversity, Perceived Personalization, Satisfaction, and Novelty). We show that a SVD algorithm scores high on accuracy and perceived personalization, while a KNN algorithm scores better on novelty. We also report findings on preferred university features.

11,626 views
13 citations
Original Research
08 July 2021
Mean rating based on the personality traits and the application domain. (A) Extraversion, (B) Agreeableness, (C) Conscientiousness, (D) Neuroticism, (E) Openness.
5,219 views
15 citations
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