{"title":"推荐系统的深度学习:文献综述和观点","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":"{\"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}","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}
Deep Learning for Recommender Systems: Literature Review and Perspectives
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.