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基于知识图谱共同邻居排序采样的推荐模型 被引量:14

Recommendation Model Based on Public Neighbor Sorting and Sampling of Knowledge Graph
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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。 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.
作者 李世宝 张益维 刘建航 崔学荣 张玉成 LI Shibao;ZHANG Yiwei;LIU Jianhang;CUI Xuerong;ZHANG Yucheng(College of Oceanography and Space Informatics,China University of Petroleum(East China),Qingdao 266580,China;CAS Engineering Laboratory for Intelligent Agricultural Machinery Equipment,Beijing 100190,China)
出处 《电子与信息学报》 EI CSCD 北大核心 2021年第12期3522-3529,共8页 Journal of Electronics & Information Technology
基金 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)。
关键词 知识图谱 推荐系统 排序采样 图卷积神经网络 Knowledge graph Recommendation system Sorted sampling Graph convolutional neural network
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