基于混合图的用户模型的可视化注释

V. Guchev, F. Cena, Fabiana Vernero, Cristina Gena
{"title":"基于混合图的用户模型的可视化注释","authors":"V. Guchev, F. Cena, Fabiana Vernero, Cristina Gena","doi":"10.1145/3320435.3320472","DOIUrl":null,"url":null,"abstract":"Structured user model data not only allow system personalization, but also may be of interest as a source for analysis: in particular, for the study of general trends and for the detection of anomalies in preferences and mutually-referenced features among different user models. Such sources are multidimensional and interrelated, and recently started to be represented as graph-based datasets. Among the most effective ways of studying such data is visual exploration based on data-driven graph drawing approaches: in particular, node-link and node-link-group diagrams. The paper provides an overview of advanced approaches to the graphical representation of multidimensional data derived from user modeling and presents a proposal for developing flexible and scalable user interfaces for the hypergraph-based visual exploration of relations within a user model (UM). Then, we propose these principles in the visualization of an existing adaptive system.","PeriodicalId":254537,"journal":{"name":"Proceedings of the 27th ACM Conference on User Modeling, Adaptation and Personalization","volume":"442 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Visual Annotations for Hybrid Graph-based User Model\",\"authors\":\"V. Guchev, F. Cena, Fabiana Vernero, Cristina Gena\",\"doi\":\"10.1145/3320435.3320472\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Structured user model data not only allow system personalization, but also may be of interest as a source for analysis: in particular, for the study of general trends and for the detection of anomalies in preferences and mutually-referenced features among different user models. Such sources are multidimensional and interrelated, and recently started to be represented as graph-based datasets. Among the most effective ways of studying such data is visual exploration based on data-driven graph drawing approaches: in particular, node-link and node-link-group diagrams. The paper provides an overview of advanced approaches to the graphical representation of multidimensional data derived from user modeling and presents a proposal for developing flexible and scalable user interfaces for the hypergraph-based visual exploration of relations within a user model (UM). Then, we propose these principles in the visualization of an existing adaptive system.\",\"PeriodicalId\":254537,\"journal\":{\"name\":\"Proceedings of the 27th ACM Conference on User Modeling, Adaptation and Personalization\",\"volume\":\"442 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-06-07\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 27th ACM Conference on User Modeling, Adaptation and Personalization\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3320435.3320472\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 27th ACM Conference on User Modeling, Adaptation and Personalization","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3320435.3320472","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

摘要

结构化的用户模型数据不仅允许系统个性化,而且还可以作为分析的来源:特别是,用于研究一般趋势和检测不同用户模型之间的偏好和相互引用特征中的异常。这些数据源是多维的、相互关联的,最近开始被表示为基于图的数据集。研究此类数据的最有效方法之一是基于数据驱动的图形绘制方法的视觉探索:特别是节点链接图和节点链接组图。本文概述了来自用户建模的多维数据的图形化表示的高级方法,并提出了为用户模型(UM)中基于超图的关系可视化探索开发灵活和可扩展的用户界面的建议。然后,我们在现有的自适应系统的可视化中提出了这些原则。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Visual Annotations for Hybrid Graph-based User Model
Structured user model data not only allow system personalization, but also may be of interest as a source for analysis: in particular, for the study of general trends and for the detection of anomalies in preferences and mutually-referenced features among different user models. Such sources are multidimensional and interrelated, and recently started to be represented as graph-based datasets. Among the most effective ways of studying such data is visual exploration based on data-driven graph drawing approaches: in particular, node-link and node-link-group diagrams. The paper provides an overview of advanced approaches to the graphical representation of multidimensional data derived from user modeling and presents a proposal for developing flexible and scalable user interfaces for the hypergraph-based visual exploration of relations within a user model (UM). Then, we propose these principles in the visualization of an existing adaptive system.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Adaptive Modelling of Attentiveness to Messaging: A Hybrid Approach Engagement, Metrics and Personalisation: the Good, the Bad and the Ugly Towards Social Choice-based Explanations in Group Recommender Systems Personalized Gait-based Authentication Using UWB Wearable Devices Towards Utter Well-Being: Personalization for Guardian Angels
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1