Abstract
To improve the movie box office prediction accuracy, this paper proposes an adaptive attention with consumer sentinel (LSTM-AACS) for movie box office prediction. First, the influencing factors of the movie box office are analyzed. Tackling the problem of ignoring consumer groups in existing prediction models, we add consumer features and then quantitatively analyze and normalize the box office influence factors. Second, we establish an LSTM (Long Short-Term Memory) box office prediction model and inject the attention mechanism to construct an adaptive attention with consumer sentinel for movie box office prediction. Finally, 10,398 pieces of movie box office dataset are used in the Kaggle competition to compare the prediction results with the LSTM-AACS model, LSTM-Attention model, and LSTM model. The results show that the relative error of LSTM-AACS prediction is 6.58%, which is lower than other models used in the experiment.
Highlights
Complexity capture the input affective information more vigorously, thereby further improving the prediction accuracy
Tackling the current movie box office prediction problem, this paper proposes an adaptive attention LSTM model with consumer sentinel
(1) To improve the movie box office prediction accuracy, this paper proposes an LSTM model with an AAM and consumer sentinel (LSTM-AACS)
Summary
Complexity capture the input affective information more vigorously, thereby further improving the prediction accuracy. The model is based on the LSTM model injecting the adaptive attention (AAM) with consumer sentinel. (1) To improve the movie box office prediction accuracy, this paper proposes an LSTM model with an AAM and consumer sentinel (LSTM-AACS). Injecting an adaptive attention can capture affective input information, which provides a guarantee for the accuracy of the movie box office prediction results. It can better capture consumer characteristics, thereby improving prediction accuracy. E LSTM-AACS model is applied to the prediction of the movie box office and achieves good results. e results show that the relative error of LSTM-AACS prediction is 6.58%, which is lower than other models used in the experiment
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