knowledge-graphnamed-entity-recognitionquestion-answeringknowledge-managementrelation-extraction UpdatedSep 18, 2023 Python KGAT: Knowledge Graph Attention Network for Recommendation, KDD2019 knowledge-graphrecommender-systemgraph-attention-networksgraph-neural-networkskdd2019high-order-connectivityknowledge-based-reco...
knowledge graph,user-item profile,recommendation system Paper References: [1] Cai H, Zheng V W, Chang K.A comprehensive survey of graph embedding:problems, techniques and applications[J].IEEE Transactions on Knowledge andData Engineering, 2018. ...
代码:github.com/huangtinglin 1. introduction Knowledge Graph-based Intent Network (KGIN) is a recommendation framework, which consists of three components: (1)user Intent modeling, (2)relational path-aware aggregation, (3)indepedence modeling. 2. problem formulation 数据:user-item interactions + ...
论文链接地址: 论文代码链接地址: code摘要 在本文中,我们为知识感知推荐系统引入了一种新的自监督合理化方法,称为 KGRec。为了有效地识别信息知识联系,我们提出了一种细心的知识合理化机制,为知识三元组生…
知识图谱词嵌入(Knowledge Graph Embedding,KGE)模型:如图6的右侧部分,将知识图谱三元组中的前2个(电影ID和关系实体)作为输入,预测出第3个(目标实体)。 图6 MKR框架 在3个子模型中,最关键的是交叉压缩单元模型。下面就先从该模型开始一步一步地实现MKR框架。
Multi-modal Knowledge Graphs for Recommender Systems(CIKM 2020)[将多模态信息引入知识图谱推荐,整体结构上包括多模态知识图谱graph embedding和recommendation两部分,其中graph embedding包括entity encoder和attention,用于综合表示节点以及其周围节点信息。] 知识计算 Representation(知识表示) 知识应用的基础,目前分布式表示或...
As the largest knowledge graph in materials science to date, MatKG provides structured organization of domain-specific data. Its deployment holds promise for various applications, including material discovery, recommendation systems, and advanced analytics....
a unified framework for DTI prediction by combining knowledge graph (KG) and recommendation system. This framework firstly learns a low-dimensional representation for various entities in the KG, and then integrates the multimodal information via neural factorization machine (NFM). KGE_NFM is evaluated...
ZJU-DAILY/MetaKG: Source code for MetaKG: Meta-learning on Knowledge Graph for Cold-start Recommendation. TKDE 2022. (github.com) 解决问题 Challenge I:现有的KG依赖大量的用户-项目的反馈,但是当用户-项目稀缺,KG的性能就下降。所以设计了meta结构捕获了高阶用户-项目交互信息。
natural-language-processinginformation-retrievalpapersurveyknowledge-graphquestion-answeringrepresentation-learningcross-modalknowledge-graph-completionnerdialogue-systemsreasoningrelation-extractioncommonsensetemporal-knowledge-graphrecommendation-systemsmeta-relational-learning ...