用海报视觉信息增强关键词，降低票房预测误差14.5%
Copycat vs. Original: Multi-modal Pretraining and Variable Importance in Box-office Prediction
- 将用户描述词与海报视觉信息对齐，提升关键词表征
- 模型使票房预测误差降低14.5%，显著优于基线
- 发现模仿热门片可提升票房，但竞争越强效果越弱
电影产业风险高，亟需自动化工具辅助票房预测。本文构建一个复杂的多模态神经网络，通过将观众提供的电影描述关键词与电影海报的视觉信息对齐，增强关键词表征，使票房预测误差降低14.5%。该模型可用于分析“模仿类电影”（copycat movies）的商业可行性，即近期上映的与成功影片高度相似的电影。通过计算此类特征在预测中的影响，发现模仿行为与票房正相关，但当相似影片数量增多或内容重合度提高时，该效应减弱。本研究开发了先进的深度学习工具，为电影行业提供重要商业洞察。
原文摘要 · Abstract (English)
The movie industry is associated with an elevated level of risk, which necessitates the use of automated tools to predict box-office revenue and facilitate human decision-making. In this study, we build a sophisticated multimodal neural network that predicts box offices by grounding crowdsourced descriptive keywords of each movie in the visual information of the movie posters, thereby enhancing the learned keyword representations, resulting in a substantial reduction of 14.5% in box-office prediction error. The advanced revenue prediction model enables the analysis of the commercial viability of "copycat movies," or movies with substantial similarity to successful movies released recently. We do so by computing the influence of copycat features in box-office prediction. We find a positive relationship between copycat status and movie revenue. However, this effect diminishes when the number of similar movies and the similarity of their content increase. Overall, our work develops sophisticated deep learning tools for studying the movie industry and provides valuable business insight.
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