Kei Shimonishi, H. Kawashima, Ryo Yonetani, Erina Ishikawa, T. Matsuyama
{"title":"Learning aspects of interest from Gaze","authors":"Kei Shimonishi, H. Kawashima, Ryo Yonetani, Erina Ishikawa, T. Matsuyama","doi":"10.1145/2535948.2535955","DOIUrl":null,"url":null,"abstract":"This paper presents a probabilistic framework to model the gaze generative process when a user is browsing a content consisting of multiple regions. The model enables us to learn multiple aspects of interest from gaze data, to represent and estimate user's interest as a mixture of aspects, and to predict gaze behavior in a unified framework. We recorded gaze data of subjects when they were browsing a digital pictorial book, and confirmed the effectiveness of the proposed model in terms of predicting the gaze target.","PeriodicalId":403097,"journal":{"name":"GazeIn '13","volume":"35 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2013-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"GazeIn '13","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/2535948.2535955","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 4
Abstract
This paper presents a probabilistic framework to model the gaze generative process when a user is browsing a content consisting of multiple regions. The model enables us to learn multiple aspects of interest from gaze data, to represent and estimate user's interest as a mixture of aspects, and to predict gaze behavior in a unified framework. We recorded gaze data of subjects when they were browsing a digital pictorial book, and confirmed the effectiveness of the proposed model in terms of predicting the gaze target.
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