[1]王俊淑,张国明,胡 斌.基于深度学习的推荐算法研究综述[J].南京师范大学学报(工程技术版),2018,(04):033.[doi:10.3969/j.issn.1672-1292.2018.04.006]
 Wang Junshu,Zhang Guoming,Hu Bin.A Survey of Deep Learning Based Recommendation Algorithms[J].Journal of Nanjing Normal University(Engineering and Technology),2018,(04):033.[doi:10.3969/j.issn.1672-1292.2018.04.006]
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基于深度学习的推荐算法研究综述
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南京师范大学学报(工程技术版)[ISSN:1006-6977/CN:61-1281/TN]

卷:
期数:
2018年04期
页码:
033
栏目:
计算机工程
出版日期:
2018-12-30

文章信息/Info

Title:
A Survey of Deep Learning Based Recommendation Algorithms
文章编号:
1672-1292(2018)04-0033-11
作者:
王俊淑12张国明34胡 斌12
(1.南京师范大学地理科学学院,江苏 南京 210023)(2.南京师范大学虚拟地理环境教育部重点实验室,江苏 南京 210023)(3.南京大学计算机科学与技术系,江苏 南京 210023)(4.江苏省卫生统计信息中心,江苏 南京 210008)
Author(s):
Wang Junshu12Zhang Guoming34Hu Bin12
(1.School of Geography,Nanjing Normal University,Nanjing 210023,China)(2.Key Laboratory of Virtual Geographic Environment of Ministry of Education,Nanjing Normal University,Nanjing 210023,China)(3.Department of Computer Science and Technology,Nanjing University,Nanjing 210023,China)(4.Health Statistics and Information Center of Jiangsu Province,Nanjing 210008,China)
关键词:
推荐系统深度学习协同过滤内容推荐动态推荐标签推荐
Keywords:
recommender systemsdeep learningcollaborative filteringcontent-based recommendationdynamic recommendationtag-based recommendation
分类号:
TP3-05
DOI:
10.3969/j.issn.1672-1292.2018.04.006
文献标志码:
A
摘要:
深度学习技术是机器学习领域的一个研究热点,已被深入研究并广泛应用于许多领域. 推荐系统是缓解信息过载的重要技术,如何将深度学习融入推荐系统,利用深度学习的优势从各种复杂多维数据中学习用户和物品的内在本质特征,构建更加符合用户兴趣需求的模型,以提高推荐算法的性能和用户满意度,是深度学习应用于推荐系统的主要研究任务. 对基于深度学习的推荐算法研究和应用现状进行了综述,讨论并展望了深度学习应用于推荐系统的研究发展趋势.
Abstract:
Deep learning technology has recently become a very hot topic in the machine learning field,and has been thoroughly studied and widely used in many fields. The recommender system is an important technology to alleviate information overload. Deep learning has the advantages of learning the intrinsic characteristics of users and items from various complex multidimensional data. How to integrate deep learning into recommender systems to build a model that is more in line with the user preferences and improving recommendation performance is the main research task of deep learning based recommender systems. In this paper,the research and application of deep learning based recommender algorithms are reviewed,and the future research and development trends of integrating deep learning to recommender systems are discussed.

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备注/Memo

备注/Memo:
收稿日期:2018-04-07.
基金项目:国家自然科学基金(41571389)、江苏省自然科学基金(BK20171037)、江苏省高校自然科学研究面上项目(17KJB420003).
通讯联系人:张国明,工程师,研究方向:推荐系统、健康大数据. E-mail:zgmming@qq.com
更新日期/Last Update: 2018-12-30