Bayesian dual neural networks for recommendation

doi:10.1007/s11704-018-8049-1

CORC > 计算技术研究所 > 中国科学院计算技术研究所 > 中国科学院计算技术研究所期刊论文 > 英文

	Bayesian dual neural networks for recommendation
	He, Jia 2,3; Zhuang, Fuzhen 2,3; Liu, Yanchi 1; He, Qing 2,3; Lin, Fen 4
刊名	FRONTIERS OF COMPUTER SCIENCE
	2019-12-01
卷号	13 期号:6 页码:1255-1265
关键词	collaborative filtering Bayesian neural network hybrid recommendation algorithm
ISSN号	2095-2228
DOI	10.1007/s11704-018-8049-1
英文摘要	Most traditional collaborative filtering (CF) methods only use the user-item rating matrix to make recommendations, which usually suffer from cold-start and sparsity problems. To address these problems, on the one hand, some CF methods are proposed to incorporate auxiliary information such as user/item profiles; on the other hand, deep neural networks, which have powerful ability in learning effective representations, have achieved great success in recommender systems. However, these neural network based recommendation methods rarely consider the uncertainty of weights in the network and only obtain point estimates of the weights. Therefore, they maybe lack of calibrated probabilistic predictions and make overly confident decisions. To this end, we propose a new Bayesian dual neural network framework, named BDNet, to incorporate auxiliary information for recommendation. Specifically, we design two neural networks, one is to learn a common low dimensional space for users and items from the rating matrix, and another one is to project the attributes of users and items into another shared latent space. After that, the outputs of these two neural networks are combined to produce the final prediction. Furthermore, we introduce the uncertainty to all weights which are represented by probability distributions in our neural networks to make calibrated probabilistic predictions. Extensive experiments on real-world data sets are conducted to demonstrate the superiority of our model over various kinds of competitors.
资助项目	National Key R&D Program of China[2018YFB1004300] ; National Natural Science Foundation of China[61773361] ; National Natural Science Foundation of China[61473273] ; National Natural Science Foundation of China[91546122] ; Science and Technology Project of Guangdong Province[2015B010109005] ; Project of Youth Innovation Promotion Association CAS[2017146] ; WeChat cooperation project
WOS研究方向	Computer Science
语种	英语
出版者	HIGHER EDUCATION PRESS
WOS记录号	WOS:000475801700008
内容类型	期刊论文
源URL	[http://119.78.100.204/handle/2XEOYT63/4475]
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Zhuang, Fuzhen
作者单位	1.Rutgers State Univ, Newark, NJ 07102 USA 2.Chinese Acad Sci, Inst Comp Technol, Key Lab Intelligent Informat Proc, Beijing 100190, Peoples R China 3.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 4.WeChat Search Applicat Dept, Search Prod Ctr, Beijing 100080, Peoples R China
推荐引用方式 GB/T 7714	He, Jia,Zhuang, Fuzhen,Liu, Yanchi,et al. Bayesian dual neural networks for recommendation[J]. FRONTIERS OF COMPUTER SCIENCE,2019,13(6):1255-1265.
APA	He, Jia,Zhuang, Fuzhen,Liu, Yanchi,He, Qing,&Lin, Fen.(2019).Bayesian dual neural networks for recommendation.FRONTIERS OF COMPUTER SCIENCE,13(6),1255-1265.
MLA	He, Jia,et al."Bayesian dual neural networks for recommendation".FRONTIERS OF COMPUTER SCIENCE 13.6(2019):1255-1265.