Computer Science

Computer Science ›› 2021, Vol. 48 ›› Issue (11): 176-183.doi: 10.11896/jsjkx.201000004

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

Collaborative Filtering Recommendation Algorithm of Behavior Route Based on Knowledge Graph

CHEN Yuan-yi1,3, FENG Wen-long2,3, HUANG Meng-xing2,3, FENG Si-ling2,3   

  1. 1 College of Computer Science and Cyberspace Security,Hainan University,Haikou 570228,China
    2 College of Information Science & Technology,Hainan University,Haikou 570228,China
    3 State Key Laboratory of Marine Resource Utilization in South China Sea,Hainan University,Haikou 570228,China
  • Received:2020-10-01 Revised:2021-01-19 Online:2021-11-15 Published:2021-11-10
  • About author:CHEN Yuan-yi,born in 1995,postgra-duate.His main research interests include data mining and big data analysis.
    FENG Wen-long,born in 1968,Ph.D,professor,Ph.D supervisor,is a professional member of China Computer Fe-deration.His main research interests include big data and smart services.
  • Supported by:
    National Key R & D Project(2018YFB1404400).

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Abstract: For personalized recommendation,common recommendation algorithms include content recommendation,Item CF and User CF.However,most of these algorithms and their improved algorithms tend to focus on users' explicit feedback (tags,ra-tings,etc.) or rating data,and lack the use of multi-dimensional user behavior and behavior order,resulting in low recommendation accuracy and cold start problems.In order to improve the recommendation accuracy,a collaborative filtering recommendation algorithm based on knowledge graph (BR-CF) is proposed.Firstly,according to the user behavior data,behavior graph and behavior route are created considering the behavior order,and then the vectorization technology (Keras Tokenizer) is used.Finally,the similarity between multi-dimensional behavior route vectors is calculated,and the route collaborative filtering recommendation is carried out for each dimension.On this basis,two improved algorithms combining BR-CF and Item CF are proposed.The expe-rimental results show that the BR-CF algorithm can recommend effectively in multiple dimensions on the user behavior dataset of Ali Tianchi,realize the full utilization of data and the diversity of recommendation,and the improved algorithm can improve the recommendation performance of Item CF.

Key words: Behavior graph, Behavior order, Behavior route, Multi-dimensional recommendation, Recommendation algorithm, Route coordination

CLC Number: 

  • TP391
[1]CHANG L,ZHANG W T,GU T L,et al.Review of recommendation systems based on knowledge graph[J].CAAI Transactions on Intelligent Systems,2019,14(2):207-216.
[2]CAI W L,ZHENG J B,PAN W K,et al.Neighborhood-En-hanced Transfer Learning for One-Class Collaborative Filtering[J].Neurocomputing,2019,341(14):80-87.
[3]PENG F,LU X,MA C,et al.Multi-level preference regression for cold-start recommendations[J].International Journal of Machine Learning and Cybernetics,2018,9(7):1117-1130.
[4]CORTES D.Cold-start recommendations in Collective MatrixFactorization[J].arXiv:1809.03666,2018.
[5]WANG C D,DENG Z H,LAI J H,et al.Serendipitous Recommendation in E-Commerce Using Innovator-Based Collaborative Filtering[J].IEEE Transactions on Cybernetics,2018,49(7):2678-2692.
[6]CAI F,WANG S,RIJKE M D.Behavior-based personalization in web search[J].Journal of the Association for Information Science &Technology,2017,68(4):855-868.
[7]HUANG X Y,XIONG L Y,LI Q D.Personalized news recommendation technology based on improved collaborative filtering algorithm[J].Journal of Sichuan University(Natural Science Edition),2018,55(1):49-55.
[8]HUANG D X.Research on user dynamic interest model inrecommendation system [D].Guangzhou:South China University of Technology,2018.
[9]SHEN D D,WANG H T,JIANG Y,et al.A next recommendation algorithm based on knowledge map and short-term prefe-rence[J].Minicomputer System,2020(4):849-854.
[10]CHEN X,XU H,ZHANG Y,et al.Sequential Recommendation with User Memory Networks[C]//The Eleventh ACM International Conference.ACM,2018.
[11]LI W J.Research on time aware recommendation algorithm[D].Chengdu:University of Electronic Science and Technology,2017.
[12]KANG J Y,SU F J.Long and short interest recommendation model based on generative countermeasure network[J].Computer Technology and Development,2020,30(6):35-39.
[13]ZHANG Z P,SHEN X Y.Research on User Behavior Recommendation Method Based on Deep Learning[J].Computer Engineering and Applications,2019,55(4):142-147,158.
[14]GUI Z Y,ZHANG Y M,LI W W.Research on Learning Resource Recommendation Algorithm Based on behavior sequence analysis[J].Computer Application Research,2020,37(7):1979-1982.
[15]DUAN W Q.Prediction of online purchasing behavior based on user behavior sequence[D].Nanchang:Jiangxi University of Finance and Economics,2019.
[16]CHAI L,XU H F,LUO Z M,et al.A multi-source heteroge-neous data analytic method for future price fluctuation prediction[J].Neurocomputing,2020,418:11-20.
[17]HUANG M X,LI M L,HAN H R.Research on entity recognition and knowledge mapping based on electronic medical record[J].Computer Application Research,2019,36(12):3735-3739.
[18]SHAO B L,LI X J,BIAN G Q.A survey of research hotspots and frontier trends of recommendation systems from the perspective of knowledge graph[J].Expert Systems With Applications,2020:113764.
[19]CHE J Q,XIE H W.Hierarchical collaborative filtering recommendation algorithm based on spark[J].Electronic technology application,2015,41(9):135-138.
[20]HAN D,SHEN X,GAN T,et al.A Dynamic Individual Recommendation Method Based on ReinforcementLearning[C]//International Symposium on Parallel Architecture,Algorithm and Programming.2017.
[21]LI C,HU W L.Exploration on experience model of recommendation system－taking video recommendation as an example[J].Industrial Design Research,2018(5):81-85.
[22]SYAEKHONI M A,LEE C,KEON Y S.Analyzing customer behavior from shopping path data using operation edit distance[J].Applied Intelligence,2016,48:1912-1932.
[23]WANG H,XIA Z Q.Research on recommendation algorithmbased on ant colony algorithm and browsing path[J].China Science and Technology Information,2009(7):103-104.
[24]XIONG Y R.Recommendation algorithm based on user behavior trajectory [D].Chengdu:University of Electronic Science and Technology of China,2013.
[25]LEI M L.Research on shopping behavior based on Alibaba bigdata[J].Internet of Things Technology,2016,6(5):57-60.
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