Intelligent Recommendation System for Online English Teaching Based on Artificial Intelligence

According to the school's training plan and curriculum standards, formulating reasonable and personal English learning knowledge recommendations to students is an important research work at present. This paper proposes a group recommendation algorithm TAGR which combines time series and attention mechanism. In the model, GRU (gate current unit) is used to model the evolution of user interest with time, capture the transfer process of user interest from time series, and accurately represent user interest preferences through aggregation. At the same time, the attention mechanism is used to fuse group preferences, and the representative users in the group are strengthened by dynamic weighting, and the group preferences are modeled by using the interaction information between items and groups. The experimental results show that the proposed English teaching recommendation model is superior to the traditional model and improves the accuracy of group recommendation.