基于循环知识图谱的虚拟社区知识动态推荐方法

doi:10.13229/j.cnki.jdxbgxb20210846

Abstract

Abstract:

For the disadvantage that the number of resources in digital communities is huge and it is difficult for users to find the required data easily and quickly at the first time, a dynamic recommendation method for virtual community knowledge was proposed based on cyclic knowledge graphs. The knowledge point neighborhood entities in the virtual community were treated as contexts to obtain knowledge expression learning entities. The cyclic knowledge graph was fused with the data to be recommended, the user's historical click information was calculated, and the entity feature vector was extracted. Meanwhile, the user model in the virtual community with several neural co?filtering layers was established, implicitly interacting users and knowledge point relationships. The implicit vector of users to be recommended and the implicit vector of knowledge points were nonlinearly transformed several times to complete dynamic recommendation. The experiments prove that the recommendation satisfaction is high, and the recommendation results are comprehensive and not homogenized.

Key words: circular knowledge map, virtual community knowledge, dynamic recommendation, feature extraction, user modeling

CLC Number:

TP391

Cai-mao LI,Shao-fan CHEN,Cheng-rong LIN,Yu-quan HOU,Hao LI. Dynamic recommendation method of virtual community knowledge based on circular knowledge map[J].Journal of Jilin University(Engineering and Technology Edition), 2022, 52(10): 2385-2390.

Figures/Tables 4

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Dynamic recommendation method of virtual community knowledge based on circular knowledge map

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