Teaching Vertical Network Learning Resource Recommendation Based on LSTM and Collaborative Filtering

To address the negative impact of information overload, an analysis is conducted on learning resource recommendation methods in teaching vertical networks. Firstly, the gating mechanism in long short-term memory networks is utilized to reflect learners’ learning and forgetting. Meanwhile, attention mechanism is combined to capture the impact of different difficulty levels on knowledge tracking. A knowledge tracking model using long short-term memory network is constructed. Then, combining model-based collaborative filtering algorithms and attention mechanisms, a collaborative filtering-based learning resource recommendation model is constructed. The proposed knowledge tracking model performed better in precision, recall, and F1 score, with scores of 92.43%, 91.37%, and 92.16%, respectively. On the Assisment2012 and RAIEd2020 datasets, the proposed model’s area under the curve was 82.37% and 81.54%, respectively. The complete model’s performance was the best. The model without attention mechanism had the smallest area under the curve, which was 72.14% and 70.46%, respectively. The proposed learning resource recommendation model performed the best for recommendation precision, recall, and F1 score. The learning resource recommendation list’s diversity was good. These research results contribute to improving learning outcomes and promoting the sustainable development of teaching vertical networks.