李世宝¹,
张益维^1,,,
刘建航¹,
崔学荣¹,
张玉成²
1.中国石油大学(华东)海洋与空间信息学院 青岛 266580
2.中国科学院智能农业机械装备工程实验室 北京 100190
基金项目:国家自然科学基金(61972417, 61872385, 91938204)，国家重点研发计划(2017YFC1405203)，中国科学院科技服务网络计划(KFJ-STS-ZDTP-074)，中央高校基本科研业务费专项资金(18CX02134A, 19CX05003A-4, 18CX02137A)

详细信息

作者简介:李世宝：男，1978年生，硕士，副教授，硕士生导师，研究方向为移动计算、无线通信
张益维：男，1995年生，硕士生，研究方向为知识图谱推荐技术
刘建航：男，1978年生，博士，副教授，研究方向为车联网
崔学荣：男，1979年生，博士，教授，研究方向为智能感知
张玉成：男，1980年生，博士，副研究员，研究方向为智能信息处理

通讯作者:张益维 yiwei9084@gmail.com

中图分类号:TP391.1, TP311

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被引次数:0

出版历程

收稿日期:2020-08-21
修回日期:2021-01-14
网络出版日期:2021-01-19
刊出日期:2021-12-21

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

Shibao LI¹,
Yiwei ZHANG^1,,,
Jianhang LIU¹,
Xuerong CUI¹,
Yucheng ZHANG²
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)


摘要
摘要:知识图谱作为辅助信息可以有效缓解传统推荐模型的冷启动问题。但在提取结构化信息时，现有模型都忽略了图谱中实体之间的邻居关系。针对这一问题，该文提出一种基于共同邻居排序采样的知识图谱卷积网络(KGCN-PN)推荐模型，该模型首先基于共同邻居数目对知识图谱中的每个实体邻域进行排序采样；其次利用图卷积神经网络沿着图谱中的关系路径将实体自身信息与接收域信息逐层融合；最后将用户特征向量与融合得到的实体特征向量送入预测函数中预测用户与实体项目交互的概率。实验结果表明该模型在数据稀疏场景下相较其他基线模型性能均获得了相应提升。
关键词:知识图谱/
推荐系统/
排序采样/
图卷积神经网络
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.
Key words:Knowledge graph/
Recommendation system/
Sorted sampling/
Graph convolutional neural network



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