Computer Science

Computer Science ›› 2021, Vol. 48 ›› Issue (4): 78-84.doi: 10.11896/jsjkx.200400023

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

Recommendation Algorithm Based on Bipartite Graph Convolution Representation

XIONG Xu-dong1, DU Sheng-dong1,2,3, XIA Wan-jun1, LI Tian-rui1,2,3   

  1. 1 School of Information Science and Technology,Southwest Jiaotong University,Chengdu 611756,China
    2 Institute of Artificial Intelligence,Southwest Jiaotong University,Chengdu 611756,China
    3 National Engineering Laboratory of Integrated Transportation Big Data Application Technology,Chengdu 611756,China
  • Received:2020-06-24 Revised:2020-09-20 Online:2021-04-15 Published:2021-04-09
  • About author:XIONG Xu-dong,born in 1995,postgraduate.His main research interests include recommendation algorithms and network representation learning.(asia123dong@126.com)
    LI Tian-rui,born in 1969,Ph.D,professor,Ph.D supervisor,is a distinguished member of China Computer Federation.His main research interests include big data intelligence,rough sets and granular computing.
  • Supported by:
    National Key R&D Program of China(2017YFB1401400).

PDF (PC)

1251

Abstract: With the rapid development of data-driven intelligent technology,personalized intelligent recommendation algorithms and related applications become research hotspots.Recommendation can be regarded as a matching problem between users and items.As the semantic gap between users and items,it’s inconvenient to match them directly.Many existing recommendation methods based on deep learning use the idea of mapping entities from different spaces into a unified semantic space to calculate the matching degree by embedding representation.With the emergence of network representation learning,the bipartite graph can be formed between users and items and the embedding representation in the recommendation algorithm can also be regarded as nodes representation in bipartite graph.Many recommendation algorithms based on bipartite graph nodes representation have been proposed.However,existing algorithms are still hard to extract high-order interactive information effectively.To solve this problem,a bipartite graph convolution representation-based recommendation algorithm(BGCRRA) is developed in this paper.Interactions between users and items are regarded as a bipartite graph at first,nodes are represented by adaptively fusing multi-order and multi-level graphs secondly,and finally the matching degree of users and items is calculated and the recommendation is realized.Comparative experiments are carried out on 3 open datasets and the effectiveness of the proposed algorithm is verified by comparing HR and NDCG(Normalized Discounted Cumulative Gain) indexes of our algorithm and the state-of-the-art algorithms.

Key words: Bipartite graph, Embedding methods, Graph convolution, Recommendation algorithms

CLC Number: 

  • TP391
[1]CHENG X Q,JIN X L,WANG Y Z,et al.Survey on Big Data System and Analytic Technology[J].Journal of Software,2014,25(9):1889-1908.
[2]WANG G X,LIU H P.Survey of personalized recommendation system[J].Computer Engineering and Applications,2012,48(7):66-76.
[3]HUANG L W,JIANG B T,LV S Y,et al.Survey on Deep Learning Based Recommender System[J].Journal of Computer Science and Technology,2018,41(7):1619-1647.
[4]ZHOU B.Research on Relationship Between Bipartite Network Recommendation Algorithm and Collaborative Filtering Algorithm[J].Computer Science,2019,46(S2):163-166,177.
[5]ZHOU T,REN J,MEDO M,et al.Bipartite network projection and personal recommendation[J].Physical Review E,2007,76(4):046115.
[6]ZHOU B,YANG C F.The Research Progress of Recommendation Algorithm Based on Bipartite Network[J].Technology Intelligence Engineering,2016,2(1):77-90.
[7]ZHOU J,CUI G,ZHANG Z,et al.Graph Neural Networks:A Review of Methods and Applications[J].arXiv:1812.08434,2018.
[8]DEFFERRARD M,BRESSON X,VANDERGHEYNST P,et al.Convolutional neural networks on graphs with fast localized spectral filtering[C]//Neural Information Processing Systems.2016:3844-3852.
[9]KIPF T,WELLING M.Semi-Supervised Classification withGraph Convolutional Networks[J].arXiv:1609.02907,2016.
[10]WANG X,HE X,WANG M,et al.Neural Graph Collaborative Filtering[C]//Proceedings of the International ACM SIGIR Conference on Research and Development in Information Retrieval.2019:165-174.
[11]BERG R,KIPF T N,WELLING M.Graph convolutional matrix completion[J].arXiv:1706.02263,2017.
[12]ZHENG L,LU C,JIANG F,et al.Spectral collaborative filtering[C]//Proceedings of the 12th ACM Conference on Recommender Systems.2018:311-319.
[13]HAMILTON W L,YING Z,LESKOVEC J,et al.InductiveRepresentation Learning on Large Graphs[C]//Neural Information Processing Systems.2017:1024-1034.
[14]WU L,SUN P,HONG R,et al.SocialGCN:An Efficient Graph Convolutional Network based Model for Social Recommendation[J].arXiv:1811.02815,2018.
[15]WU L,SUN P,FU Y,et al.A Neural Influence Diffusion Model for Social Recommendation[C]//Proceedings of the International ACM SIGIR Conference on Research and Development in Information Retrieval.2019:235-244.
[16]YING R,HE R,CHEN K,et al.Graph Convolutional Neural Networks for Web-Scale Recommender Systems[C]//Proceedings of the Knowledge Discovery and Data Mining.2018:974-983.
[17]DENG Z H,HUANG L,WANG C D,et al.Deepcf:A unified framework of representation learning and matching function learning in recommender system[C]//Proceedings of the AAAI Conference on Artificial Intelligence.2019:61-68.
[18]XUE F,HE X,WANG X,et al.Deep Item-based Collaborative Filtering for Top-N Recommendation[J].ACM Transactions on Information Systems,2019,37(3):1-25.
[19]ELKAHKY A,SONG Y,HE X,et al.A Multi-View DeepLearning Approach for Cross Domain User Modeling in Recommendation Systems[C]//Proceedings of the Web Conference.2015:278-288.
[20]COVINGTON P,ADAMS J,SARGIN E.Deep neural networks for Youtube recommendations[C]//Proceedings of the 10th ACM Conference on Recommender Systems.ACM,2016:191-198.
[21]XU Z,CHEN C,LUKASIEWICZ T,et al.Tag-aware personalized recommendation using a deep-semantic similarity model with negative sampling[C]//Proceedings of the 25th ACM Conference on Information and Knowledge Management.ACM,2016:1921-1924.
[22]CHENG H T,KOC L,HARMSEN J,et al.Wide & deep learning for recommender systems[C]//Proceedings of the 1st Workshop on Deep Learning for Recommender Systems.ACM,2016:7-10.
[23]XUE H,DAI X,ZHANG J,et al.Deep matrix factorizationmodels for recommender systems[C]//Proceedings of the International Joint Conference on Artificial Intelligence.2017:3203-3209.
[24]HE X,LIAO L,ZHANG H,et al.Neural Collaborative Filtering[C]//Proceedings of the Web Conference.2017:173-182.
[25]VELICKOVIC P,CUCURULL G,CASANOVA A,et al.Graph Attention Networks[C]//Proceedings of the International Conference on Learning Representations.2018:1-12.
[26]VASWANI A,SHAZEER N,PARMAR N,et al.Attention is all you need[C]//Neural Information Processing Systems.2017:6000-6010.
[27]GILMER J,SCHOENHOLZ S,RILEY P,et al.Neural message passing for Quantum chemistry[C]//Proceedings of the International Conference on Machine Learning.2017:1263-1272.
[28]HE X,ZHANG H,KAN M,et al.Fast Matrix Factorization for Online Recommendation with Implicit Feedback[C]//Proceedings of the International ACM SIGIR Conference on Research and Development in Information Retrieval.2016:549-558.
[1] WANG Ming, PENG Jian, HUANG Fei-hu. Multi-time Scale Spatial-Temporal Graph Neural Network for Traffic Flow Prediction [J]. Computer Science, 2022, 49(8): 40-48.
[2] TAN Ying-ying, WANG Jun-li, ZHANG Chao-bo. Review of Text Classification Methods Based on Graph Convolutional Network [J]. Computer Science, 2022, 49(8): 205-216.
[3] LI Jian-zhi, WANG Hong-ling, WANG Zhong-qing. Automatic Generation of Patent Summarization Based on Graph Convolution Network [J]. Computer Science, 2022, 49(6A): 172-177.
[4] SHAO Yan-hua, LI Wen-feng, ZHANG Xiao-qiang, CHU Hong-yu, RAO Yun-bo, CHEN Lu. Aerial Violence Recognition Based on Spatial-Temporal Graph Convolutional Networks and Attention Model [J]. Computer Science, 2022, 49(6): 254-261.
[5] ZHAO Xiao-hu, YE Sheng, LI Xiao. Multi-algorithm Fusion Behavior Classification Method for Body Bone Information Reconstruction [J]. Computer Science, 2022, 49(6): 269-275.
[6] LI Zi-yi, ZHOU Xia-bing, WANG Zhong-qing, ZHANG Min. Stance Detection Based on User Connection [J]. Computer Science, 2022, 49(5): 221-226.
[7] GAO Yue, FU Xiang-ling, OUYANG Tian-xiong, CHEN Song-ling, YAN Chen-wei. EEG Emotion Recognition Based on Spatiotemporal Self-Adaptive Graph ConvolutionalNeural Network [J]. Computer Science, 2022, 49(4): 30-36.
[8] TAN Zhen-qiong, JIANG Wen-Jun, YUM Yen-na-cherry, ZHANG Ji, YUM Peter-tak-shing, LI Xiao-hong. Personalized Learning Task Assignment Based on Bipartite Graph [J]. Computer Science, 2022, 49(4): 269-281.
[9] ZHOU Hai-yu, ZHANG Dao-qiang. Multi-site Hyper-graph Convolutional Neural Networks and Application [J]. Computer Science, 2022, 49(3): 129-133.
[10] LI Hao, ZHANG Lan, YANG Bing, YANG Hai-xiao, KOU Yong-qi, WANG Fei, KANG Yan. Fine-grained Sentiment Classification of Chinese Microblogs Combining Dual Weight Mechanismand Graph Convolutional Neural Network [J]. Computer Science, 2022, 49(3): 246-254.
[11] PAN Zhi-hao, ZENG Bi, LIAO Wen-xiong, WEI Peng-fei, WEN Song. Interactive Attention Graph Convolutional Networks for Aspect-based Sentiment Classification [J]. Computer Science, 2022, 49(3): 294-300.
[12] XIE Yu, YANG Rui-ling, LIU Gong-xu, LI De-yu, WANG Wen-jian. Human Skeleton Action Recognition Algorithm Based on Dynamic Topological Graph [J]. Computer Science, 2022, 49(2): 62-68.
[13] MIAO Qi-guang, XIN Wen-tian, LIU Ru-yi, XIE Kun, WANG Quan, YANG Zong-kai. Graph Convolutional Skeleton-based Action Recognition Method for Intelligent Behavior Analysis [J]. Computer Science, 2022, 49(2): 156-161.
[14] ZHANG Hu, BAI Ping. Graph Convolutional Networks with Long-distance Words Dependency in Sentences for Short Text Classification [J]. Computer Science, 2022, 49(2): 279-284.
[15] GAN Chuang, WU Gui-xing, ZHAN Qing-yuan, WANG Peng-kun, PENG Zhi-lei. Multi-scale Gated Graph Convolutional Network for Skeleton-based Action Recognition [J]. Computer Science, 2022, 49(1): 181-186.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!
渝ICP备16013121号-4
Add:18# Honghu West Rd., Yubei ,Chongqing,China    Post Code: 401121
Tel: 023-63500828/023-67039612/023-67039623
E-mail: jsjkx12@163.com
Powered by Beijing Magtech Co., Ltd.