推荐系统的实用表示学习

O. Zakharchuk
{"title":"推荐系统的实用表示学习","authors":"O. Zakharchuk","doi":"10.1145/3176349.3176900","DOIUrl":null,"url":null,"abstract":"The ability to provide high quality personalized recommendations is among the most significant types of competitive advantage an online business can have. However, even having vast amounts of data, creating a recommender system is far from being trivial. This tutorial covers applying deep learning models for creating robust item and user representations for personalized recommender systems, as well as some of the typical problems encountered when working on production recommender systems and possible solutions for these problems.","PeriodicalId":198379,"journal":{"name":"Proceedings of the 2018 Conference on Human Information Interaction & Retrieval","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2018-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Practical Representation Learning for Recommender Systems\",\"authors\":\"O. Zakharchuk\",\"doi\":\"10.1145/3176349.3176900\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The ability to provide high quality personalized recommendations is among the most significant types of competitive advantage an online business can have. However, even having vast amounts of data, creating a recommender system is far from being trivial. This tutorial covers applying deep learning models for creating robust item and user representations for personalized recommender systems, as well as some of the typical problems encountered when working on production recommender systems and possible solutions for these problems.\",\"PeriodicalId\":198379,\"journal\":{\"name\":\"Proceedings of the 2018 Conference on Human Information Interaction & Retrieval\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-03-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 2018 Conference on Human Information Interaction & Retrieval\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3176349.3176900\",\"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 2018 Conference on Human Information Interaction & Retrieval","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3176349.3176900","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

提供高质量的个性化推荐的能力是在线业务可以拥有的最重要的竞争优势之一。然而,即使有大量的数据,创建一个推荐系统也绝非小事。本教程涵盖了应用深度学习模型为个性化推荐系统创建健壮的项目和用户表示,以及在生产推荐系统中遇到的一些典型问题以及这些问题的可能解决方案。
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
Practical Representation Learning for Recommender Systems
The ability to provide high quality personalized recommendations is among the most significant types of competitive advantage an online business can have. However, even having vast amounts of data, creating a recommender system is far from being trivial. This tutorial covers applying deep learning models for creating robust item and user representations for personalized recommender systems, as well as some of the typical problems encountered when working on production recommender systems and possible solutions for these problems.
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
Distant Voices in the Dark: Understanding the Incongruent Information Needs of Fiction Authors and Readers Visualizing and Exploring Scientific Literature with CiteSpace: An Introduction What Sources to Rely on:: Laypeople's Source Selection in Online Health Information Seeking Investigating Everyday Information Behavior of Using Ambient Displays: A Case of Indoor Air Quality Monitors Collaborative Information Seeking through Social Media Updates in Real-Time
×
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