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
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