关键词: 网络搜索数据, 机器学习, 票房预测

Abstract: Movie box office prediction has always been an important and complex task for the relevant departments of industry management. Film-related variables are complex and difficult to choose objectively, and the difficulty of data acquisition restricts the main factors of this kind of models. In contrast, web search data is structured data released by Internet companies to record the search behavior of netizens. It has clear meaning, easy-access, and can reflect the development trend of things objectively and timely. Based on the web search data, a hybrid prediction model including data selection method and machine learning algorithm is established in this study. Firstly, the optimal training set is constructed by selecting the training data with the greatest similarity to the test set. Secondly, Imperialist Competition Algorithms is applied to select the best combination of parameters of Least Squares Support Vector Machine. Finally, the optimized model is used for prediction. In order to test the stability and applicability, an empirical study is carried out using the box office data of the films released in mainland China in 2017, which shows that the proposed hybrid model has higher prediction accuracy. The built model is suitable for box office prediction and can provide decision-making reference for industry management departments.

Key words: web search data, machine learning, box office prediction