Computer Science ›› 2020, Vol. 47 ›› Issue (10): 114-120.doi: 10.11896/jsjkx.190900038

• Database & Big Data & Data Science • Previous Articles     Next Articles

Neural Collaborative Filtering Based on Enhanced-attention Mechanism

KANG Yan, BU Rong-jing, LI Hao, YANG Bing, ZHANG Ya-chuan, CHEN Tie   

  1. School of Software,Yunnan University,Kunming 650091,China
  • Received:2019-09-04 Revised:2020-01-05 Online:2020-10-15 Published:2020-10-16
  • About author:KANG Yan,born in 1972,Ph.D,postgraduate supervisor,is a member of China Computer Federation.Her main research interests include machine learning and software engineering.
  • Supported by:
    National Natural Science Foundation of China (61762092,61762089) and Yunnan Provincial Key Laboratory of Software Engineering Open Fund Project (2017SE204)

Abstract: The recommendation system is the core to solve the problem of information overload.The existing research on recommendation framework faces many problems,such as sparse explicit feedback data and difficulty to preprocess data,especially the recommendation performances for new users and new projects need to be further improved.With the advancement of deep lear-ning,recommendation based on deep learning has become a current research hotspot.A large number of experiments have proved the effectiveness of deep learning applied to recommendation system.This paper presents EANCF (Neural Collaborative Filtering based on Enhanced-attention Mechanism) on the basis of NCF.It studies the recommendation framework from the perspective of implicit feedback data,and considers the data feature extraction by means of max-pooling,local inference modeling and combining many different ways of data fusion.Meanwhile,attention mechanism is introduced to reasonably allocate weight value for the network,reduce the loss of information and improve the performance of recommendation.Finally,based on two large real data sets,Movielens-1m and Pinterest-20,comparative experiments are carried out between EANCF and NCF,as well as some classical algorithms,and the training process of EANCF framework is given in detail.The experimental results show that the proposed EANCF framework does have good recommendation performance.Compared with the NCF,both HR@10 and NDCG@10 are significantly improved,with the highest increase of 3.53% for HR@10 and 2.47% for NDCG@10.

Key words: Deep learning, Collaborative filtering, Implicit feedback, Attention mechanism

CLC Number: 

  • TP391
[1]HE X N.Neural collaborative filtering[C]//Proceedings of the 26th International Conference on World Wide Web.2017:173-182.
[2]HE X,DU X,WANG X,et al.Outer Product-based Neural Collaborative Filtering[C]//Twenty-Seventh International Joint Conference on Artificial Intelligence.2018.
[3]BAI T,WEN J R,ZHANG J,et al.A neural collaborative filtering model with interaction-based neighborhood[C]//Proceedings of the 2017 ACM on Conference on Information and Knowledge Management.ACM,2017:1979-1982.
[4]WANG X,HE X,WANG M,et al.Neural graph collaborativefiltering[C]//Proceedings of the 42nd international ACM SIGIR conference on Research and development in Information Retrieval.2019:165-174.
[5]JIANG Y.Fusion Information Recommendation Model Based on Graph Convolution and Neural Collaborative Filtering [D].Changchun:Jilin University,2018.
[6]XIE E N,HE L M,WANG X H.Deep Collaborative Filtering Model Based on Attention Mechanism [J].Journal of Metrology University of China,2019,30(2):219-225,242.
[7]PORTEOUS I,ASUNCION A,WELLING M.Bayesian matrix factorization with side information and dirichlet process mixtures[C]//Proceedings of the Twenty-Fourth AAAI Conference on Artificial Intelligence.2010.
[8]CHEN Q,ZHU X,LING Z,et al.Enhanced LSTM for Natural Language Inference[J].arxiv:1609.06038,2016.
[9]OARD D W,KIM J.Implicit feedback for recommender systems[C]//Proceedings of the AAAI Workshop on Recommender Systems.1998.
[10]YAO W L,HE J,HUANG G Y,et al.A Graph-based model for context-aware recommendation using implicit feedback data[J].World Wide Web,2015,18(5):1351-1371.
[11]HE M,MORIMOTO Y.Capturing Temporal Dynamics of Implicit Feedbacks for Collaborative Filtering by Using Deep Recurrent Neural Networks[J].Bulletin of Networking,Computing,Systems,and Software,2018,7(1):33-37.
[12]GOLDBERG D,NICHOLS D,OKI B M,et al.Using collaborative filtering to weave an information tapestry[J].Communications of the ACM,1992,35(12):61-70.
[13]RESNICK P,IACOVOU N,SUCHAK M,et al.GroupLens:an open architecture for collaborative filtering of netnews[C]//Proceedings of the 1994 ACM Conference on Computer Supported Cooperative Work.ACM,1994:175-186.
[14]ZHAO X Y,XIA L,ZHANG L,et al.Deep reinforcement lear-ning for page-wise recommendations[C]//Proceedings of the 12th ACM Conference on Recommender Systems.2018:95-103.
[15]PASRICHA R,MCAULEY J.Translation-based factorizationmachines for sequential recommendation[C]//Proceedings of the 12th ACM Conference on Recommender Systems.2018:63-71.
[16]SUN Z,YANG J,ZHANG J,et al.Recurrent knowledge graph embedding for effective recommendation[C]//Proceedings of the 12th ACM Conference on Recommender Systems.2018:297-305.
[17]DUAN D K,FU X F.Research on User Cold Start Problem in Hybrid Collaborative Filtering Algorithm [J].CEA,2017,53(21):151-156.
[18]ZHANG X C.Utilizing Tri-training Algorithm to Solve ColdStart Problem in Recommender System[J].Computer Science,2016,43(12):108-114.
[19]DENG C B,YU H Q,FAN G S.Integrating Dynamic Collaborative Filtering and Deep Learning for Recommendation[J].Computer Science,2019,46(8):28-34.
[20]ZHENG L.A survey and critique of deep learning on recommender systems[J].Chicago:University of Illinois,2016:31.
[21]LONG C,ZHANG H,XIAO J,et al.SCA-CNN:Spatial andChannel-Wise Attention in Convolutional Networks for Image Captioning[C]//IEEE Conference on Computer Vision & Pattern Recognition.2017:5659-5667.
[22]BAHDANAU D,CHO K,BENGIO Y.Neural Machine Translation by Jointly Learning to Align and Translate[J].arxiv:1409.0473,2014.
[23]LI J,REN P,CHEN Z,et al.Neural attentive session-based reco-mmendation[C]//Proceedings of the 2017 ACM on Conference on Information and Knowledge Management.2017:1419-1428.
[24]GUO X,ZHU J H.Deep Neural Network RecommendationModel Based on User Vectorization Representation and Attention Mechanism[J].Computer Science,2019,46(8):111-115.
[25]DACREMA M F,CREMONESI P,JANNACH D.Are we really making much progress? A worrying analysis of recent neural recommendation approaches[C]//Proceedings of the 13th ACM Conference on Recommender Systems.ACM,2019:101-109.
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