Helping university students to choose elective courses by using a hybrid
  multi-criteria recommendation system with genetic optimization
A. Esteban A. Zafra C. Romero
The wide availability of specific courses together with the flexibility of
academic plans in university studies reveal the importance of Recommendation
Systems (RSs) in this area. These systems appear as tools that help students to
choose courses that suit to their personal interests and their academic
performance. This paper presents a hybrid RS that combines Collaborative
Filtering (CF) and Content-based Filtering (CBF) using multiple criteria
related both to student and course information to recommend the most suitable
courses to the students. A Genetic Algorithm (GA) has been developed to
automatically discover the optimal RS configuration which include both the most
relevant criteria and the configuration of the rest of parameters. The
experimental study has used real information of Computer Science Degree of
University of Cordoba (Spain) including information gathered from students
during three academic years, counting on 2500 entries of 95 students and 63
courses. Experimental results show a study of the most relevant criteria for
the course recommendation, the importance of using a hybrid model that combines
both student information and course information to increase the reliability of
the recommendations as well as an excellent performance compared to previous
models.