Time series analysis of language learner behavior data and personalized recommendation algorithm

Aiming at the time series characteristics of language learner behavior data and the demand for personalized recommendation, this study proposes a recommendation algorithm that combines collaborative filtering and long short-term memory network (LSTM). The sliding window method is used to extract time series features such as learning time and question accuracy, and to construct a dynamic model of user behavior. Experimental results show that the algorithm performs well in terms of root mean square error (RMSE) and precision @10, with RMSE reaching 0.324 and Precision @10 being 0.782, which is significantly better than traditional collaborative filtering and a single LSTM model. Visual analysis reveals the periodicity and trend of user behavior, and verifies the effectiveness of the algorithm in capturing dynamic preferences. This study provides an accurate personalized recommendation solution for online language learning platforms, demonstrates the application value of time series analysis in improving learning experience, and provides new ideas for data-driven decision-making in the field of educational technology.