LSTM-RPA: A Simple but Effective Long Sequence Prediction Algorithm for Music Popularity Prediction
This is an incremental improvement for music industry analysts needing better long-term trend predictions.
The paper tackled the problem of predicting long-term trends in music popularity by proposing LSTM-RPA, which improved F scores by up to 18.52% compared to baseline models like LSTM and ARIMA.
The big data about music history contains information about time and users' behavior. Researchers could predict the trend of popular songs accurately by analyzing this data. The traditional trend prediction models can better predict the short trend than the long trend. In this paper, we proposed the improved LSTM Rolling Prediction Algorithm (LSTM-RPA), which combines LSTM historical input with current prediction results as model input for next time prediction. Meanwhile, this algorithm converts the long trend prediction task into multiple short trend prediction tasks. The evaluation results show that the LSTM-RPA model increased F score by 13.03%, 16.74%, 11.91%, 18.52%, compared with LSTM, BiLSTM, GRU and RNN. And our method outperforms tradi-tional sequence models, which are ARIMA and SMA, by 10.67% and 3.43% improvement in F score.Code: https://github.com/maliaosaide/lstm-rpa