Deep Learning for Recommender Systems: Literature Review and Perspectives

B. Selma, Boustia Narhimene, Rezoug Nachida
{"title":"Deep Learning for Recommender Systems: Literature Review and Perspectives","authors":"B. Selma, Boustia Narhimene, Rezoug Nachida","doi":"10.1109/ICRAMI52622.2021.9585931","DOIUrl":null,"url":null,"abstract":"During the last few years, deep learning revolutionized several fields including: image analysis, speech recognition and language processing. Deep learning has also become pervasive and demonstrated effectiveness in the field of recommender systems and information retrieval. Unlike the conventional recommendation systems, deep learning have the unique ability to successfully capture non-trivial and non-linear interactions between user and item, allowing for the codification of more complicated abstractions. We begin by providing a brief overview of recommender systems and deep learning. Second, we present a complete overview of the current state of the art in deep learning-based RS. Then, we describe a possible future research direction of the field. Finally, we conclude the review.","PeriodicalId":440750,"journal":{"name":"2021 International Conference on Recent Advances in Mathematics and Informatics (ICRAMI)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2021-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 International Conference on Recent Advances in Mathematics and Informatics (ICRAMI)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICRAMI52622.2021.9585931","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2

Abstract

During the last few years, deep learning revolutionized several fields including: image analysis, speech recognition and language processing. Deep learning has also become pervasive and demonstrated effectiveness in the field of recommender systems and information retrieval. Unlike the conventional recommendation systems, deep learning have the unique ability to successfully capture non-trivial and non-linear interactions between user and item, allowing for the codification of more complicated abstractions. We begin by providing a brief overview of recommender systems and deep learning. Second, we present a complete overview of the current state of the art in deep learning-based RS. Then, we describe a possible future research direction of the field. Finally, we conclude the review.
推荐系统的深度学习:文献综述和观点
在过去的几年里,深度学习彻底改变了几个领域,包括:图像分析、语音识别和语言处理。深度学习在推荐系统和信息检索领域也变得普遍和有效。与传统的推荐系统不同,深度学习具有独特的能力,可以成功捕获用户和物品之间的非平凡和非线性交互,从而允许对更复杂的抽象进行编码。我们首先简要概述推荐系统和深度学习。其次,我们对基于深度学习的RS的现状进行了全面的概述,然后描述了该领域未来可能的研究方向。最后,我们对本文进行总结。
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 求助全文
来源期刊
自引率
0.00%
发文量
0
×
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
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
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 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信