论文:A Survey on Knowledge Graph-Based Recommender Systems 首发链接: 【综述】基于知识图谱的推荐系统综述mp.weixin.qq.com/s/6lTxdxBsw-HJzuKhvNMhcg 基于知识图谱的推荐系统综述 作者信息 Elesdspline目前从事NLP与知识图谱相关工作。 导语 本文是2020年针对知识图谱作为辅助信息用于推荐系统的一篇综述。
A survey on knowledge graph-based recommender systems IEEE Trans Knowl Data Eng (2020) Google Scholar [11] W. Zhang, T. Gu, W. Sun, Y. Phatpicha, L. Chang, C. Bin Travel attractions recommendation with travel spatial-temporal knowledge graphs International conference of pioneering computer sc...
(补充说明:在A Survey on Knowledge Graph-Based Recommender Systems[1]一文中,作者把使用了Knowledge Graph的推荐系统分为三类:①可表示为矩阵运算的embedding-based methods, 直接利用KG中的信息得到user和item的latent vector,最后相乘得到偏好得分,②利用user和/或item的连接相似性的path-based methods, 连接相似的...
Thus, the knowledge graph is introduced into the recommendation domain to alleviate these problems. We collect papers related to the knowledge graph-based recommender systems in recent years to summarize their fundamental knowledge and main ideas, including the usage of the knowledge graph in the ...
目前,一些研究尝试利用KGs结构进行推荐,可分为embedding-based方法、path-based方法和两者一起(unified)方法三种类型。 Embedding-based methods. 基于嵌入的方法[23,25,27]首先使用知识图谱嵌入(KGE,Knowledge Graph Embedding)[27]算法对知识图谱进行预处理,然后在推荐框架中使用学习到的实体嵌入,将CF框架中的各类side...
Deep Learning on Knowledge Graph for Recommender System: A Survey Recent advances in research have demonstrated the effectiveness of knowledge graphs (KG) in providing valuable external knowledge to improve recommendation... Y Gao,YF Li,Y Lin,... - 《Arxiv》 被引量: 0发表: 2020年 Knowledge-...
Categorization of knowledge graph based recommendation methods and benchmark datasets from the perspectives of application scenarios: A comprehensive survey 来自 掌桥科研 喜欢 0 阅读量: 151 作者:N Khan,Z Ma,A Ullah,K Polat 摘要: Recommender Systems (RS) are established to deal with the preferences...
论文名称 :Knowledge Graph Convolutional Networks for Recommender Systems 会议:WWW2019 论文地址:https://dl_acm.xilesou.top/citation.cfm?id=3313417 论文介绍 1.论文所做工作 这篇论文将图神经网络GNN中的图卷积网络GCN模型应用到了知识图谱KG中,并运用到推荐系统中。
5.1Multi-modal Knowledge Graph Embedding Figure 4中呈现的encoder的具体信息是: 然后propagation layer的使用方式:知识图谱的结构学习是使用TransE模型(h+r≈t),然后这里的aggregation方式是不aggregation邻居=>而是aggregate多模态实体来传播更新特征。 这里的propagation是在于传播三元组的embedding,公式如下: ...
其一是feature based方法:其将多模态信息和entity信息直接拼接到一起,作为全新的entity信息,但是这种方法的缺点是需要知识图谱中的每个实体都包含对应的graph等多模态信息,这对数据集的要求过高了。 其二是entity-based方法:其将文本模态和视觉模态看作是两个entity,分别进行建模。