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.

使用混合推荐系统，结合协同过滤与基于内容过滤的方法，根据学生和课程信息，通过遗传算法自动发现最佳配置，以更可靠地推荐最适合的课程。实验结果表明，混合模型在课程推荐中的重要性，使用该模型相较于之前的模型表现出色。

使用混合多准则推荐系统与遗传优化帮助大学生选择选修课程

Helping university students to choose elective courses by using a hybrid  multi-criteria recommendation system with genetic optimization

Recommender systems have been studied for decades with numerous promising
models been proposed. Among them, Collaborative Filtering (CF) models are
arguably the most successful one due to its high accuracy in recommendation and
elimination of privacy-concerned personal meta-data from training. This paper
extends the usage of CF-based model to the task of course recommendation. We
point out several challenges in applying the existing CF-models to build a
course recommendation engine, including the lack of rating and meta-data, the
imbalance of course registration distribution, and the demand of course
dependency modeling. We then propose several ideas to address these challenges.
Eventually, we combine a two-stage CF model regularized by course dependency
with a graph-based recommender based on course-transition network, to achieve
AUC as high as 0.97 with a real-world dataset.

基于协同过滤模型的课程推荐引擎的提出与优化，解决了缺乏评级和元数据、课程注册不平衡以及课程依赖性建模等挑战，并通过基于课程转换网络的图推荐器，实现了真实数据集 AUC 值高达 0.97。