Affective Video Recommender System

Yashowardhan Soni, Cecilia Ovesdotter Alm, Reynold J. Bailey
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引用次数: 3

Abstract

Video recommendation is the task of providing users with customized media content conventionally done by considering historical user ratings. We develop classifiers that learn from human faces toward a video recommender system that utilizes displayed emotional reactions to previously seen videos for predicting preferences. We use a dataset collected from subjects who watched videos selected to elicit different emotions, to model two related problems: (1) prediction of user rating and (2) whether a user would recommend a particular video. The classifiers are trained on two forms of face-based features: facial expressions and skin-estimated pulse. In addition, the impact of data augmentation and instance size are studied.
情感视频推荐系统
视频推荐的任务是为用户提供定制的媒体内容,通常是通过考虑历史用户评级来完成的。我们开发了从人脸学习的分类器,用于视频推荐系统,该系统利用对先前看过的视频的情绪反应来预测偏好。我们使用从观看视频的受试者中收集的数据集来引发不同的情绪,以模拟两个相关问题:(1)预测用户评分;(2)用户是否会推荐特定的视频。分类器在两种基于面部的特征上进行训练:面部表情和皮肤估计脉搏。此外,还研究了数据扩充和实例大小的影响。
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