Deep Learning Based Recommendation: A Survey

被引：32

作者：

Liu, Juntao ^{[1
]}

Wu, Caihua ^{[2
]}

机构：

[1] China Shipbldg Ind Corp, Res Inst 709, Wuhan, Hubei, Peoples R China

[2] Air Force Early Warning Acad, Huang Pi NCO Sch, Sect Automat Command, Wuhan, Hubei, Peoples R China

来源：

INFORMATION SCIENCE AND APPLICATIONS 2017, ICISA 2017 | 2017年 / 424卷

基金：

中国国家自然科学基金;

关键词：

Deep learning; Recommendation; Neural network; SYSTEMS;

D O I：

10.1007/978-981-10-4154-9_52

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Due to the great success, deep learning gains much attentions in the research field of recommendation. In this paper, we review the deep learning based recommendation approaches and propose a classification framework, by which the deep learning based recommendation approaches are divided according to the input and output of the approaches. We also give the possible research directions in the future.

引用

页码：451 / 458

页数：8

共 25 条

[1] Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions [J].

Adomavicius, G ;

Tuzhilin, A .

IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, 2005, 17 (06) :734-749

[2]

[Anonymous], 2015, SIGIR

[3]

[Anonymous], INT C MACH LEARN ICM

[4]

[Anonymous], 2011, P 14 INT C ARTIFICIA

[5]

[Anonymous], 2007, P 24 INT C MACHINE L

[6]

[Anonymous], 2015, KDD

[7]

[Anonymous], 2013, NIPS

[8]

[Anonymous], 2014, RECOMMENDING MUSIC S

[9]

[Anonymous], 2014, KDD

[10] Deep Neural Networks for YouTube Recommendations [J].

Covington, Paul ;

Adams, Jay ;

Sargin, Emre .

PROCEEDINGS OF THE 10TH ACM CONFERENCE ON RECOMMENDER SYSTEMS (RECSYS'16), 2016, :191-198

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