User Perception of Situated Product Recommendations in Augmented Reality

Brandon Huynh, Adam Ibrahim, YunSuk Chang, Tobias Höllerer, J. O'Donovan
{"title":"User Perception of Situated Product Recommendations in Augmented Reality","authors":"Brandon Huynh, Adam Ibrahim, YunSuk Chang, Tobias Höllerer, J. O'Donovan","doi":"10.1142/s1793351x19400129","DOIUrl":null,"url":null,"abstract":"Augmented reality (AR) interfaces increasingly utilize artificial intelligence systems to tailor content and experiences to the user. We explore the effects of one such system — a recommender system for online shopping — which allows customers to view personalized product recommendations in the physical spaces where they might be used. We describe results of a [Formula: see text] condition exploratory study in which recommendation quality was varied across three user interface types. Our results highlight potential differences in user perception of the recommended objects in an AR environment. Specifically, users rate product recommendations significantly higher in AR and in a 3D browser interface, and show a significant increase in trust in the recommender system, compared to a web interface with 2D product images. Through semi-structured interviews, we gather participant feedback which suggests AR interfaces perform better due to their ability to view products within the physical context where they will be used.","PeriodicalId":217956,"journal":{"name":"Int. J. Semantic Comput.","volume":"19 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Int. J. Semantic Comput.","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1142/s1793351x19400129","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 5

Abstract

Augmented reality (AR) interfaces increasingly utilize artificial intelligence systems to tailor content and experiences to the user. We explore the effects of one such system — a recommender system for online shopping — which allows customers to view personalized product recommendations in the physical spaces where they might be used. We describe results of a [Formula: see text] condition exploratory study in which recommendation quality was varied across three user interface types. Our results highlight potential differences in user perception of the recommended objects in an AR environment. Specifically, users rate product recommendations significantly higher in AR and in a 3D browser interface, and show a significant increase in trust in the recommender system, compared to a web interface with 2D product images. Through semi-structured interviews, we gather participant feedback which suggests AR interfaces perform better due to their ability to view products within the physical context where they will be used.
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
增强现实中定位产品推荐的用户感知
增强现实(AR)界面越来越多地利用人工智能系统为用户定制内容和体验。我们探索了一个这样的系统——在线购物推荐系统——的效果,它允许客户在可能使用它们的物理空间中查看个性化的产品推荐。我们描述了一个[公式:见文本]条件探索性研究的结果,其中推荐质量在三种用户界面类型中是不同的。我们的研究结果突出了AR环境中用户对推荐对象感知的潜在差异。具体来说,与带有2D产品图像的web界面相比,用户在AR和3D浏览器界面中对产品推荐的评价明显更高,并且对推荐系统的信任度显著增加。通过半结构化访谈,我们收集了参与者的反馈,这些反馈表明AR界面表现更好,因为它们能够在使用产品的物理环境中查看产品。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Guest Editorial - Special Issue on IEEE AIKE 2022 TemporalDedup: Domain-Independent Deduplication of Redundant and Errant Temporal Data Knowledge Graph-Based Explainable Artificial Intelligence for Business Process Analysis Knowledge Graph-Based Integration of Autonomous Driving Datasets Confidence-Based Cheat Detection Through Constrained Order Inference of Temporal Sequences
×
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