Yashowardhan Soni, Cecilia Ovesdotter Alm, Reynold J. Bailey
{"title":"Affective Video Recommender System","authors":"Yashowardhan Soni, Cecilia Ovesdotter Alm, Reynold J. Bailey","doi":"10.1109/WNYIPW.2019.8923087","DOIUrl":null,"url":null,"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.","PeriodicalId":275099,"journal":{"name":"2019 IEEE Western New York Image and Signal Processing Workshop (WNYISPW)","volume":"190 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 IEEE Western New York Image and Signal Processing Workshop (WNYISPW)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/WNYIPW.2019.8923087","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
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.