# 基于知识图谱的推荐系统
### 基于嵌入的方法
##### item graph
| 方法 | 年份 | 论文 | 源码 |
| ---------- | ---- | ------------------------------------------------------------ | ------------------------------------ |
| CKE | 2016 | Collaborative knowledge base embedding for recommender systems | |
| DKN | 2018 | Deep Knowledge-Aware Network for News Recommendation | https://github.com/hwwang55/DKN |
| KSR | 2018 | Improving Sequential Recommendation with Knowledge-Enhanced Memory Networks | https://github.com/RUCDM/KSR |
| entity2rec | 2017 | Entity2rec: learning user-item relatedness from knowledge graphs for top-n item recommendation | https://github.com/D2KLab/entity2rec |
##### user-item graph
| 方法 | 年份 | 论文 | 源码 |
| ----- | ---- | ------------------------------------------------------------ | ------------------------------------------------------- |
| CFKG | 2018 | Learning Heterogeneous Knowledge Base Embeddings for Explainable Recommendation | https://github.com/evison/KBE4ExplainableRecommendation |
| SHINE | 2018 | Signed Heterogeneous Information Network Embedding for Sentiment Link Prediction | |
| DKFM | 2019 | Location embeddings for next trip recommendation | |
##### 其他方法
| 方法 | 年份 | 论文 | 源码 |
| ----- | ---- | ------------------------------------------------------------ | --------------------------------- |
| KTGAN | 2018 | A knowledge-enhanced deep recommendation framework incorporating gan-based models | https://github.com/ZikaiGuo/KTGAN |
| BEM | 2019 | Bayes EMbedding (BEM): Refining Representation by Integrating Knowledge Graphs and Behavior-specific Networks | |
| RCF | 2019 | Relational collaborative filtering: Modeling multiple item relations for recommendation | |
### 基于路径的方法
##### path连通性
| 方法 | 年份 | 论文 | 源码 |
| --------- | ---- | ------------------------------------------------------------ | ------------------------------------- |
| FMG | 2017 | Meta-graph based recommendation fusion over heterogeneous information networks | https://github.com/HKUST-KnowComp/FMG |
| Hete-MF | 2013 | Collaborative filtering with entity similarity regularization in heterogeneous information networks | |
| HeteRec | 2013 | Recommendation in heterogeneous information networks with implicit user feedback | |
| HeteRec_p | 2014 | Personalized entity recommendation: A heterogeneous information network approach | |
| Hete-CF | 2014 | Hete-cf: Social-based collaborative filtering recommendation using heterogeneous relations | |
| SemRec | 2015 | Semantic path based personalized recommendation on weighted heterogeneous information networks | |
| HERec | 2018 | Heterogeneous information network embedding for recommendation | https://github.com/librahu/HERec |
| RuleRec | 2019 | Jointly learning explainable rules for recommendation with knowledge graph | https://github.com/THUIR/RuleRec |
##### path嵌入
| 方法 | 年份 | 论文 | 源码 |
| ----- | ---- | ------------------------------------------------------------ | ------------------------------------------------------------ |
| MCRec | 2018 | Leveraging metapath based context for top-n recommendation with a neural co-attention model | https://github.com/librahu/MCRec |
| RKGE | 2019 | Recurrent knowledge graph embedding for effective recommendation | https://github.com/sunzhuntu/Recurrent-Knowledge-Graph-Embedding |
| KPRN | 2019 | Explainable reasoning over knowledge graphs for recommendation | https://github.com/xiangwang1223/KPRN https://github.com/terwilligers/knowledge-graph-recommender |
| PGPR | 2019 | Reinforcement knowledge graph reasoning for explainable recommendation | https://github.com/orcax/PGPR https://github.com/Jindiande/PGPR_conv2d |
| EIUM | 2019 | Explainable interaction-driven user modeling over knowledge graph for sequential recommendation | |
| Ekar | 2019 | Explainable knowledge graph-based recommendation via deep reinforcement learning | https://github.com/DeepGraphLearning/RecommenderSystems |
### 联合方法
##### 基于user历史行为
| 方法 | 年份 | 论文 | 源码 |
| --------- | ---- | ------------------------------------------------------------ | ------------------------------------------------------------ |
| RippleNet | 2018 | Ripplenet: Propagating user preferences on the knowledge graph for recommender systems | [https://github.com/hwwang55/RippleNet ](https://github.com/hwwang55/RippleNet) |
| AKUPM | 2019 | Akupm: Attentionenhanced knowledge-aware user preference model for recommendation | |
| RCoLM | 2019 | Unifying taskoriented knowledge graph learning and recommendation | |
##### 基于item多跳邻居
| 方法 | 年份 | 论文 | 源码 |
| -------- | ---- | ------------------------------------------------------------ | ------------------------------------------------------------ |
| KGCN | 2019 | Knowledge graph convolutional networks for recommender systems | [https://github.com/KanchiShimono/KGCN ](https://github.com/KanchiShimono/KGCN) |
| KGCN-LS | 2019 | Knowledge-aware graph neural networks with label smoothness regularization for recommender systems | |
| KGAT | 2019 | Kgat: Knowledge graph attention network for recommendation | [https://github.com/xiangwang1223/knowledge_graph_attention_network ](https://github.com/xiangwang1223/knowledge_graph_attention_network)[https://github.com/LunaBlack/KGAT-pytorch](https://github.com/LunaBlack/KGAT-pytorch)(包含CKE) |
| KNI | 2019 | An end-to-end neighborhood-based interaction model forknowledge-enhanced recommendation | |
| IntentGC | 2019 | Intentgc: a scalable graph convolution framework fusing heterogeneous information for recommendation | [https://github.com/peter14121/intentgc-models ](https://github.com/peter14121/intentgc-models) |
### 近年的一些方法
| 方法 | 年份 | 论文 | 源码 |
| ------- | ---- | ------------------------------------------------------------ | ------------------------------------------------------------ |
| M2GRL | 2020 | M2GRL: A Multi-task Multi-view Graph Representation Learning Framework for Web-scale Recommender Systems | [https://github.com/99731/M2GRL ](https://github.com/99731/M2GRL) |
| LR-GCCF | 2020 | Revis
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温馨提示
知识图谱是一种结构化的知识表达形式,它以图形的方式组织和存储了大量实体(如人、地点、事件等)及其相互关系。在知识图谱中,实体作为节点,实体之间的各种语义关联则通过边进行连接,形成了一个庞大的数据网络。 知识图谱的核心价值在于其能够精确、直观地表示复杂世界中的知识,并支持高效的知识查询与推理。例如,在搜索引擎中,知识图谱可以提升搜索结果的相关性和准确性,为用户提供直接的答案而非仅仅是网页链接。同时,知识图谱还能支撑高级的人工智能应用,比如问答系统、推荐系统、决策支持等领域。 构建知识图谱的过程通常包括数据抽取、知识融合、实体识别、关系抽取等多个步骤,涉及到自然语言处理、机器学习、数据库技术等多种技术手段。知识图谱的不断完善有助于实现从海量信息中挖掘深层次、有价值的知识,从而推动人工智能向着更加理解人类世界的智慧方向发展。 总之,知识图谱是一个大规模、多领域、多源异构知识集成的载体,是实现智能化信息系统的基础工具和关键基础设施,对于提升信息检索质量、推动智能应用研发具有重要作用。
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基于知识图谱的推荐系统,音乐领域知识图谱3MKG的构建.zip (155个子文件)
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core-style.css 55KB
animate.css 47KB
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basketball.mp4 1.82MB
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