玛氏:打造个人助理

Max Sklar
{"title":"玛氏:打造个人助理","authors":"Max Sklar","doi":"10.1145/2959100.2959119","DOIUrl":null,"url":null,"abstract":"Foursquare recently launched Marsbot, an SMS-based app for local recommendations. Marsbot is an intelligent friend that lives in your pocket and learns about you through the places you go in the real world. While this product is aligned with Foursquare's long-standing mission to find the best places, it represents a new era in the way people interact with recommendation engines. The promise of the latest crop of personal assistants is get us information more quickly and seamlessly, but building them comes with many challenges. In this talk, we discuss why we built Marsbot and some of the many lessons learned along the way.","PeriodicalId":315651,"journal":{"name":"Proceedings of the 10th ACM Conference on Recommender Systems","volume":"83 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2016-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Marsbot: Building a Personal Assistant\",\"authors\":\"Max Sklar\",\"doi\":\"10.1145/2959100.2959119\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Foursquare recently launched Marsbot, an SMS-based app for local recommendations. Marsbot is an intelligent friend that lives in your pocket and learns about you through the places you go in the real world. While this product is aligned with Foursquare's long-standing mission to find the best places, it represents a new era in the way people interact with recommendation engines. The promise of the latest crop of personal assistants is get us information more quickly and seamlessly, but building them comes with many challenges. In this talk, we discuss why we built Marsbot and some of the many lessons learned along the way.\",\"PeriodicalId\":315651,\"journal\":{\"name\":\"Proceedings of the 10th ACM Conference on Recommender Systems\",\"volume\":\"83 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2016-09-07\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 10th ACM Conference on Recommender Systems\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/2959100.2959119\",\"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 10th ACM Conference on Recommender Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/2959100.2959119","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1

摘要

Foursquare最近推出了一款基于短信的本地推荐应用Marsbot。Marsbot是一个聪明的朋友,住在你的口袋里,通过你在现实世界中去的地方了解你。虽然这款产品符合Foursquare寻找最佳地点的长期使命,但它代表了人们与推荐引擎互动方式的新时代。最新一批个人助理的承诺是让我们更快、更无缝地获取信息,但制造它们面临许多挑战。在这次演讲中,我们将讨论我们创建Marsbot的原因以及在此过程中获得的一些经验教训。
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
Marsbot: Building a Personal Assistant
Foursquare recently launched Marsbot, an SMS-based app for local recommendations. Marsbot is an intelligent friend that lives in your pocket and learns about you through the places you go in the real world. While this product is aligned with Foursquare's long-standing mission to find the best places, it represents a new era in the way people interact with recommendation engines. The promise of the latest crop of personal assistants is get us information more quickly and seamlessly, but building them comes with many challenges. In this talk, we discuss why we built Marsbot and some of the many lessons learned along the way.
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
Opening Remarks Mining Information for the Cold-Item Problem Are You Influenced by Others When Rating?: Improve Rating Prediction by Conformity Modeling Contrasting Offline and Online Results when Evaluating Recommendation Algorithms Intent-Aware Diversification Using a Constrained PLSA
×
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