Adaptive Attention with Consumer Sentinel for Movie Box Office Prediction

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.

Optimizing Film Companies’ Marketing Strategy Using Blockchain and Recurrent Neural Network Model

“…ird, the marketing and promotion methods are single, and the new media and other means are not fully utilized for promotion. It is necessary to use various methods, such as the Internet and other advanced technologies, to promote and market films to increase their popularity and the box office [12].…”