Using Association Rules for Course Recommendation
Narimel Bendakir, Aïmeur Esma · 2006
Students often need guidance in choosing adequate courses to complete their academic degrees. Course recommender systems have been suggested in the lit-erature as a tool to help students make informed course selections. Although a variety of techniques have been proposed in these course recommender systems, com-bining data mining with user ratings in order to improve the recommendation has never been done before. This paper presents RARE, a course Recommender system based on Association RulEs, which incorporates a data mining process together with user ratings in recommen-dation. Starting from a history of real data, it discov-ers significant rules that associate academic courses fol-lowed by former students. These rules are later used to infer recommendations. In order to benefit from the current students ’ opinions, RARE also offers to users the possibility to rate the recommendations, thus leading to an improvement of the rules. Therefore, RARE com-bines the benefits of both former students ’ experience and current students ’ ratings in order to recommend the most relevant courses to its users.