Recommendation Model Based on Public Neighbor Sorting and Sampling of Knowledge Graph

Shibao LI; Yiwei ZHANG; Jianhang LIU; Xuerong CUI; Yucheng ZHANG

doi:10.11999/JEIT200735

Volume 43 Issue 12

Dec. 2021

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Article Navigation > Journal of Electronics & Information Technology > 2021 > 43(12): 3522-3529

Shibao LI, Yiwei ZHANG, Jianhang LIU, Xuerong CUI, Yucheng ZHANG. Recommendation Model Based on Public Neighbor Sorting and Sampling of Knowledge Graph[J]. Journal of Electronics & Information Technology, 2021, 43(12): 3522-3529. doi: 10.11999/JEIT200735

Citation:

Shibao LI, Yiwei ZHANG, Jianhang LIU, Xuerong CUI, Yucheng ZHANG. Recommendation Model Based on Public Neighbor Sorting and Sampling of Knowledge Graph[J]. Journal of Electronics & Information Technology, 2021, 43(12): 3522-3529. doi: 10.11999/JEIT200735

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Recommendation Model Based on Public Neighbor Sorting and Sampling of Knowledge Graph

doi: 10.11999/JEIT200735

1.
College of Oceanography and Space Informatics, China University of Petroleum (East China), Qingdao 266580, China
2.
CAS Engineering Laboratory for Intelligent Agricultural Machinery Equipment, Beijing 100190, China

Funds: The National Natural Science Foundation of China(61972417, 61872385, 91938204), The National Key Research and Development Project(2017YFC1405203), The CAS Science and Technology Service Network Initiative(KFJ-STS-ZDTP-074), The Fundamental Research Funds for the Central Universities(18CX02134A, 19CX05003A-4, 18CX02137A)

Received Date: 2020-08-21
Rev Recd Date: 2021-01-14

Available Online: 2021-01-19

Publish Date: 2021-12-21

Abstract

Abstract

The knowledge graph as auxiliary information can effectively alleviate the cold start problem of traditional recommendation models. But when extracting structured information, the existing models ignore the neighbor relationship between entities in the graph. To solve this problem, a recommendation model based on KnowledgeGraph Convolutional Networke-Public Neighbor (KFCN-PN) sorting sampling is proposed. The model first sorts and samples each entity’s neighborhood in the knowledge graph based on the number of public neighbors; Secondly, it uses graph convolutional neural networks to integrate the entity’s own information and the receiving domain information along the graph’s relationship path layer by layer; Finally, the user feature vector and the entity feature vector obtained by the fusion are sent to the prediction function to predict the probability of the user interacting with the entity item. The experimental results show that the performance of this model is improved compared with other baseline models in data sparse scenarios.
- Knowledge graph,
- Recommendation system,
- Sorted sampling,
- Graph convolutional neural network

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References(17)

References

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