毕业设计 基于Python+Flask的知识图谱的推荐系统，音乐领域知识图谱3MKG源码+详细文档+全部数据资料 高分项目

# 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