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Sparsity Based Hybrid Prediction Model for Film Scoring

In the past several years, prediction models have played an important role in various fields, such as finance, medical treatment, industrial production and so on. Film score prediction is one of the focuses, which impacts on audience and film investors. Generally, dataset optimization is used to improve the film score prediction accuracy in the existing models, however it increases the requirement on the diversity of dataset attributes. This paper proposed a hybrid prediction model based on data sparsity. The proposed model improves the prediction accuracy through analyzing the sparsity of the original dataset. The experiment shows that the proposed model achieves a precise prediction of film score with MAE 0.394, which is lower than the others'.