Course Recommendation Model in Academic Social Networks Based on Association Rules and Multi -similarity
Xiaoxian Huang, Yong Tang, Rong Qu, Chunying Li, Chengzhe Yuan, Saimei Sun, Bixia Xu · 2018
Compared with traditional course websites, the open online course platforms have a larger number of courses, and course recommendation is becoming increasingly important. In this paper, we shall propose a course recommendation model based on academic social networks, a hybrid method combing with association rules algorithm and an improved multi-similarity algorithm of multi-source information, which can recommend courses according to potential relationships between courses and users implicit interests. The proposed model is applied to SCHOLAT. Judging from our experimental results, the new model is capable of reducing cold-start problem and providing better accuracy.