DCCR: Deep Collaborative Conjunctive Recommender for Rating Prediction

doi:10.1109/ACCESS.2019.2915531

	DCCR: Deep Collaborative Conjunctive Recommender for Rating Prediction
	Wang, Qingxian 1; Peng, Binbin 2; Shi, Xiaoyu2 ; Shang, Tianqi 3; Shang, Mingsheng2
刊名	IEEE ACCESS
	2019
卷号	7 页码:60186-60198
关键词	Recommender systems collaborative filtering rating prediction denoising autoencoders multi layered perceptron
ISSN号	2169-3536
DOI	10.1109/ACCESS.2019.2915531
通讯作者	Shang, Mingsheng(msshang@cigit.ac.cn)
英文摘要	Recently, collaborative filtering combined with various kinds of deep learning models is appealing to recommender systems, which have shown a strong positive effect in an accuracy improvement. However, many studies related to deep learning model rely heavily on abundant information to improve prediction accuracy, which has stringent data requirements in addition to raw rating data. Furthermore, most of them ignore the interaction effect between users and items when building the recommendation model. To address these issues, we propose DCCR, a deep collaborative conjunctive recommender, for rating prediction tasks that are solely based on the raw ratings. A DCCR is a hybrid architecture that consists of two different kinds of neural network models (i.e., an autoencoder and a multilayered perceptron). The main function of the autoencoder is to extract the latent features from the perspectives of users and items in parallel, while the multilayered perceptron is used to represent the interaction between users and items based on fusing the user and item latent features. To further improve the performance of DCCR, an advanced activation function is proposed, which can be specified with input vectors. The extensive experiments conducted with two well-known real-world datasets and performances of the DCCR with varying settings are analyzed. The results demonstrate that our DCCR model outperforms other state-of-art methods. We also discuss the performance of the DCCR with additional layers to show the extensibility of our model.
资助项目	National Natural Science Foundation of China[61872065] ; National Natural Science Foundation of China[91646114] ; National Natural Science Foundation of China[61602434] ; Chongqing Science Technology Bureau[cstc2017zdcy-zdyfX0076] ; Chongqing Science Technology Bureau[cstc2018jszx-cyztzxX0025]
WOS研究方向	Computer Science ; Engineering ; Telecommunications
语种	英语
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
WOS记录号	WOS:000468674500001
内容类型	期刊论文
源URL	[http://119.78.100.138/handle/2HOD01W0/7883]
专题	中国科学院重庆绿色智能技术研究院
通讯作者	Shang, Mingsheng
作者单位	1.Univ Elect Sci & Technol China, Sch Informat & Software Engn, Chengdu 610054, Sichuan, Peoples R China 2.Chinese Acad Sci, Chongqing Inst Green & Intelligent Technol, Chongqing Key Lab Big Data & Intelligent Comp, Chongqing 400714, Peoples R China 3.Sichuan Univ, Coll Comp Sci, Chengdu 610207, Sichuan, Peoples R China
推荐引用方式 GB/T 7714	Wang, Qingxian,Peng, Binbin,Shi, Xiaoyu,et al. DCCR: Deep Collaborative Conjunctive Recommender for Rating Prediction[J]. IEEE ACCESS,2019,7:60186-60198.
APA	Wang, Qingxian,Peng, Binbin,Shi, Xiaoyu,Shang, Tianqi,&Shang, Mingsheng.(2019).DCCR: Deep Collaborative Conjunctive Recommender for Rating Prediction.IEEE ACCESS,7,60186-60198.
MLA	Wang, Qingxian,et al."DCCR: Deep Collaborative Conjunctive Recommender for Rating Prediction".IEEE ACCESS 7(2019):60186-60198.