├── .gitignore ├── kg-reco ├── ProPPR-Rec.pdf ├── RippleNet.pptx ├── README.md ├── embedding-based methods.md └── path-based methods.md ├── README.md └── rl-reco └── README.md /.gitignore: -------------------------------------------------------------------------------- 1 | 2 | .DS_Store 3 | -------------------------------------------------------------------------------- /kg-reco/ProPPR-Rec.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Songweiping/Recommendation-reading-list/HEAD/kg-reco/ProPPR-Rec.pdf -------------------------------------------------------------------------------- /kg-reco/RippleNet.pptx: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Songweiping/Recommendation-reading-list/HEAD/kg-reco/RippleNet.pptx -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Recommendation-reading-list 2 | 3 | Deprecated, moved to a new [project](https://github.com/DeepGraphLearning/RecommenderSystems/blob/master/readingList.md). 4 | 5 | -------------------------------------------------------------------------------- /kg-reco/README.md: -------------------------------------------------------------------------------- 1 | ## KG-based recommendation 2 | 3 | - RippleNet: Propagating User Preferences on the Knowledge Graph for Recommender Systems. CIKM'18. ([paper](https://arxiv.org/abs/1803.03467)) 4 | -------------------------------------------------------------------------------- /rl-reco/README.md: -------------------------------------------------------------------------------- 1 | ## RL-based recommendation 2 | 3 | * Optimal Radio Channel Recommendations with Explicit and Implicit Feedback. RecSys'12. ([paper](http://www.inf.unibz.it/~ricci/papers/fp024-moling.pdf)) 4 | * An MDP-Based Recommender System. JMLR'05. ([paper](http://www.jmlr.org/papers/volume6/shani05a/shani05a.pdf)) 5 | * Factored MDPs for Detecting Topics of User Sessions. RecSys'14. ([paper](http://ml3.leuphana.de/publications/recsy160-tavakolATS.pdf)) 6 | * Deep Reinforcement Learning for List-wise Recommendations. 2018. ([paper](https://arxiv.org/pdf/1801.00209.pdf)) 7 | * Deep Reinforcement Learning for Page-wise Recommendations. RecSys'18. ([paper](https://arxiv.org/pdf/1805.02343.pdf)) 8 | * DRN: A Deep Reinforcement Learning Framework for News Recommendation. KDD'18. ([paper](http://www.personal.psu.edu/~gjz5038/paper/www2018_reinforceRec/www2018_reinforceRec.pdf)) 9 | * Recommendations with Negative Feedback via Pairwise Deep Reinforcement Learning. KDD'18. ([paper](https://arxiv.org/pdf/1802.06501.pdf)) 10 | * Reinforcement Learning to Rank with Markov Decision Process. SIGIR'17. ([paper](http://www.bigdatalab.ac.cn/~junxu/publications/SIGIR2017_RL_L2R.pdf)) 11 | * Supervised Reinforcement Learning with Recurrent Neural Network for Dynamic Treatment Recommendation. 2018. ([paper](https://arxiv.org/pdf/1807.01473.pdf)) 12 | -------------------------------------------------------------------------------- /kg-reco/embedding-based methods.md: -------------------------------------------------------------------------------- 1 | 1. Matrix factorization techniques for recommender systems. IEEE'09. ([paper](https://datajobs.com/data-science-repo/Recommender-Systems-[Netflix].pdf)) 2 | 2. A Matrix Factorization Technique with Trust Propagation for Recommendation in Social Networks 3 | . RecSys'10. ([paper](https://dl.acm.org/citation.cfm?id=1864736)) 4 | 3. SHINE: Signed Heterogeneous Information Network Embedding for Sentiment Link Prediction. WSDM'18. ([paper](https://dl.acm.org/citation.cfm?doid=3159652.3159666)) 5 | 4. Collaborative Knowledge Base Embedding for Recommender Systems. KDD'16. ([paper](https://dl.acm.org/citation.cfm?id=2939673)) 6 | 5. Question Answering over Freebase with Multi-Column Convolutional Neural Networks. ACL'2015. ([paper](http://www.aclweb.org/anthology/P15-1026)) 7 | 6. Combining Knowledge with Deep Convolutional Neural Networks for Short Text Classification. IJCAI'17. ([paper](http://yellowstone.cs.ucla.edu/~jinwang/jinwang_files/ijcai2017.pdf)) 8 | 7. DKN: Deep Knowledge-Aware Network for News Recommendation. ([paper](https://arxiv.org/abs/1801.08284)) 9 | 8. Knowledge graph embedding: A survey of approaches and applications. IEEE'17. ([paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=8047276)) 10 | 9. Learning Heterogeneous Knowledge Base Embeddings for Explainable Recommendation. Arxiv. ([paper](Learning Heterogeneous Knowledge Base 11 | Embeddings for Explainable Recommendation)) 12 | 10. Convolutional 2D Knowledge Graph Embeddings. Arxiv. ([paper](https://arxiv.org/abs/1707.01476))([code])(Multihop on server) 13 | -------------------------------------------------------------------------------- /kg-reco/path-based methods.md: -------------------------------------------------------------------------------- 1 | # Path-based Methods 2 | 3 | 1. RippleNet: Propagating User Preferences on the Knowledge Graph for Recommender Systems. CIKM'18. ([paper](https://arxiv.org/abs/1803.03467))([code](https://github.com/hwwang55/RippleNet)) 4 | 2. Personalized Entity Recommendation: A Heterogeneous Information Network Approach. WSDM'14.([paper](http://hanj.cs.illinois.edu/pdf/wsdm14_xyu.pdf)) 5 | 3. Meta-Graph Based Recommendation Fusion over Heterogeneous Information Networks.KDD'17([paper](http://www.cse.ust.hk/~hzhaoaf/data/kdd17-paper.pdf))([code](https://github.com/HKUST-KnowComp/FMG)) 6 | 4. Personalized Recommendations using Knowledge Graphs: A Probabilistic Logic Programming Approach.RecSys'16. ([paper](https://www.cs.cmu.edu/~wcohen/postscript/recsys-2016.pdf)) 7 | 5. PathSim: Meta Path-Based Top-K Similarity Search in Heterogeneous Information Networks.VLDB'11.([paper](http://vldb.org/pvldb/vol4/p992-sun.pdf)) 8 | 6. Semantic Path based Personalized Recommendation on Weighted Heterogeneous Information Networks.CIKM'15. ([paper](http://shichuan.org/doc/24.pdf)) 9 | 7. Recommendation in Heterogeneous Information Networks with Implicit User Feedback. RecSys'13. ([paper](http://hanj.cs.illinois.edu/pdf/recsys13_xyu.pdf)) 10 | 8. GO FOR A WALK AND ARRIVE AT THE ANSWER: REASONING OVER PATHS IN KNOWLEDGE BASES USING REINFORCEMENT LEARNING. Arxiv. ([paper](https://arxiv.org/abs/1711.05851))(MINERVA) 11 | 9. Multi-Hop Knowledge Graph Reasoning with Reward Shaping. Arxiv. ([paper](https://arxiv.org/abs/1808.10568v1))(Multihop) 12 | 10. Differentiable Learning of Logical Rules for Knowledge Base Reasoning. NIPS2017. ([paper](https://arxiv.org/abs/1702.08367))(NeuralLP) 13 | 11. Explainable Reasoning over Knowledge Graphs for Recommendation. AAAI'19. ([paper](https://arxiv.org/pdf/1811.04540.pdf))(KPRN) 14 | --------------------------------------------------------------------------------