ICRA: A study of highly accurate course recommendation models incorporating false review filtering and ERNIE 3.0.

Abstract

The rapid expansion of online education platforms has led to an influx of false reviews, complicating users' ability to identify suitable courses promptly. Addressing these challenges, this paper introduces ICRA (Intelligent Course Review Analysis), a novel model that identifies and filters false reviews using a custom sentiment lexicon and a pre-trained ERNIE 3.0 model. ICRA enhances data quality by analyzing user reviews and course profiles comprehensively for recommendation purposes. The model utilizes the BERT lexicon and ERNIE 3.0 to obtain deep semantic representations. It integrates BiLSTM with a multi-head attention mechanism to capture essential review details, aiming to minimize overfitting and enhance generalization. By predicting user review scores and verifying review authenticity, ICRA boosts recommendation accuracy and robustness, addressing the cold-start issue. Experimental findings highlight ICRA's excellence in predicting user ratings and delivering precise course recommendations efficiently. This capability streamlines course selection on online education platforms, improving learning experiences and efficiency.