Interpretable Educational Recommendation: An Open Framework based on Bayesian Principal Component Analysis

Yue Yun, Huan Dai, Yupei Zhang, Shuangshuang Wei, Xuequn Shang

Abstract: Recommendations in the educational environment aim to help learner access their personalized demands efficiently. Unlike commodity recommendation, limited to the ethics of pedagogy and the high cost of bad recommendations, the credibility and interpretability of the education recommendation system are more worthy of attention to achieve recommendation accuracy. However, few studies focused on the interpretability of recommendations. Thus, this study proposes an Open Recommendation framework for Interpretability based on the Bayesian principal component analysis (PPCA), ORec4Int. ORec4Int helps learners understand the recommendation by building a mapping between educational resources and the latent factors/features of learners. The interpretability will enhance his/her trust in the education recommendation system. Finally, We not only evaluate the recommendation performance of ORec4Int based on one real-world dataset but also compared its performance in interpretability and the education expert solution. Results show that ORec4Int can approach the performance of education expert solutions. Ultimately, ORec4Int is faster, more efficient, and less costly.