隨著科技的發展，多媒體的資訊量與日俱增，成為一種傳遞訊息與想法的重要通訊管道，對個人與社會帶來各方面的影響，個人而言，在接收端的我們因為不同的人格特質、喜好，可能誘發出不同的情緒感受；對社會而言，多媒體可以創造商業價值，或用來增加社會議題的討論，因此能同時了解多媒體富含的意義與受眾的反應，更能達到多媒體傳輸的目的，人工智慧技術的興起，使得我們可以多面向、量化的討論多媒體與個人、社會的關係。
在此篇研究中我們探討觀看影片時受眾的即時反應，在兩個實驗中分別以社群與個人的角度進行研究，實驗一利用電影預告的內容與受眾觀看預告時臉部表情的反應建立電影票房預測模型，並提出基於電影種類的鑲嵌表徵學習架構（Intra-genre projection）以提升票房預測準確率。實驗二我們更深入研究影片內容與觀眾反應的關係，考慮到個人的差異，建立個人化的情緒反應預測模型，並結合推薦系統(Recommendation System)的概念，運用基於影片資訊的方法與實驗一提出的種類鑲嵌表徵學習架構，在即使只有一個人少量的資訊下驗證可行性。總結實驗一與實驗二可以發現人的反應因為影片的類型而有差異，且在影片的設計上為了激發不同的情緒，各類型的影片會有不同的特徵，因此透過以類型為基礎的鑲嵌表徵學習架構，先區分出不同的類型，再進行票房或情緒反應可以增進預測準確率。

With the improvement in technology, multimedia contents increase rapidly. Multimedia is a communication medium for people convey certain concepts or emotion, and effects individuals and society. For individuals, we experience certain emotion or feelings while interacting with media contents. Induced feelings and emotion are often varying due to individual difference. As for society, multimedia makes impacts to economics and politics. It can also make some social issues bring to public attention. As the rise of artificial intelligence, people use machine learning methods to analyze large-scale and comprehensive multimedia content and human behaviors.
In this work, we learn people’s just-in-time reaction while watching video clips. We design two experiments for learning the impact of multimedia on society and individuals respectively. In the first experiment, we utilize trailers content and audience reaction to build a box office prediction model. We propose a novel intra-genre embedding which project raw features to another latent space considering the genre to improve predictive power. In the second experiment, we further investigate the relation between audience and content. Considering individual differences, we build personalized models. Their natural reaction during video watching can be regarded as certain ratings or preference. Motivated by the concept of Recommendation System, we propose content-based methods using the similar genre-based projection in experiment 1 to learn personalized reaction and deal with lack of data problems. Summarizing experiment 1 and experiment 2, we find that genre plays an important role in shaping the trailer content and viewer response. By appropriately projecting content and expression features onto a minimal intra-genre representation space, it effectively mitigates the unwanted variability in the original feature space and, hence, enhances their discriminatory power in box office prediction and personalized reaction prediction.

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