WE model: A Machine Learning Model Based on Data-Driven Movie Derivatives Market Prediction

TL;DR

This paper introduces the WE model, a machine learning-based approach that predicts movie merchandising market trends with high accuracy by analyzing movie features, addressing limitations of traditional market research methods.

Contribution

The paper presents a novel integrated machine learning model for accurate prediction of the movie merchandising market, improving upon traditional analysis techniques.

Findings

Prediction accuracy reaches 72.5%

Effective market control achieved

Integrates three machine learning algorithms

Abstract

The mature development and the extension of the industry chain make the income structure of the film industry. The income of the traditional film industry depends on the box office and also includes movie merchandising, advertisement, home entertainment, book sales etc. Movie merchandising can even become more profitable than the box office. Therefore, market analysis and forecasting methods for multi-feature merchandising of multi-type films are particularly important. Traditional market research is time-consuming and labour-intensive, and its practical value is restricted. Due to the limited research method, more effective predictive analysis technology needs to be formed. With the rapid development of machine learning and big data, a large number of machine learning algorithms for predictive regression and classification recognition have been proposed and widely used in product design and industry analysis. This paper proposes a high-precision movie merchandising prediction model based on machine learning technology: WE model. This model integrates three machine learning algorithms to accurately predict the movie merchandising market. The WE model learns the relationship between the movie merchandising market and movie features by analyzing the main feature information of movies. After testing, the accuracy rate of prediction and evaluation in the merchandising market reaches 72.5%, and it has achieved a strong market control effect.