Kei Shimonishi, H. Kawashima, Ryo Yonetani, Erina Ishikawa, T. Matsuyama
{"title":"从凝视中学习兴趣的各个方面","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":"{\"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}","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}
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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