add news rec methods NAML NRMS NPA LSTUR

README.md

@@ -56,7 +56,11 @@ The table below lists the recommender algorithms currently available in the repo
 | Factorization Machine (FM) / Field-Aware FM (FFM) | [Python CPU](notebooks/02_model/fm_deep_dive.ipynb) | Content-Based Filtering | Algorithm that predict labels with user/item features |
 | FastAI Embedding Dot Bias (FAST) | [Python CPU / Python GPU](notebooks/00_quick_start/fastai_movielens.ipynb) | Collaborative Filtering | General purpose algorithm with embeddings and biases for users and items |
 | LightGBM/Gradient Boosting Tree<sup>*</sup> | [Python CPU](notebooks/00_quick_start/lightgbm_tinycriteo.ipynb) / [PySpark](notebooks/02_model/mmlspark_lightgbm_criteo.ipynb) | Content-Based Filtering | Gradient Boosting Tree algorithm for fast training and low memory usage in content-based problems |
+| Neural Recommendation with Long- and Short-term User Representations (LSTUR)<sup>*</sup> | [Python CPU / Python GPU](notebooks/00_quick_start/lstur_synthetic.ipynb) | Content-Based Filtering | Neural recommendation algorithm with long- and short-term user interest modeling |
+| Neural Recommendation with Attentive Multi-View Learning (NAML)<sup>*</sup> | [Python CPU / Python GPU](notebooks/00_quick_start/naml_synthetic.ipynb) | Content-Based Filtering | Neural recommendation algorithm with attentive multi-view learning |
 | Neural Collaborative Filtering (NCF) | [Python CPU / Python GPU](notebooks/00_quick_start/ncf_movielens.ipynb) | Collaborative Filtering | Deep learning algorithm with enhanced performance for implicit feedback |
+| Neural Recommendation with Personalized Attention (NPA)<sup>*</sup> | [Python CPU / Python GPU](notebooks/00_quick_start/npa_synthetic.ipynb) | Content-Based Filtering | Neural recommendation algorithm with personalized attention network |
+| Neural Recommendation with Multi-Head Self-Attention (NRMS)<sup>*</sup> | [Python CPU / Python GPU](notebooks/00_quick_start/nrms_synthetic.ipynbb) | Content-Based Filtering | Neural recommendation algorithm with multi-head self-attention |
 | Restricted Boltzmann Machines (RBM) | [Python CPU / Python GPU](notebooks/00_quick_start/rbm_movielens.ipynb) | Collaborative Filtering | Neural network based algorithm for learning the underlying probability distribution for explicit or implicit feedback |
 | Riemannian Low-rank Matrix Completion (RLRMC)<sup>*</sup> | [Python CPU](notebooks/00_quick_start/rlrmc_movielens.ipynb) | Collaborative Filtering | Matrix factorization algorithm using Riemannian conjugate gradients optimization with small memory consumption. |
 | Simple Algorithm for Recommendation (SAR)<sup>*</sup> | [Python CPU](notebooks/00_quick_start/sar_movielens.ipynb) | Collaborative Filtering | Similarity-based algorithm for implicit feedback dataset |

notebooks/00_quick_start/README.md

@@ -11,7 +11,11 @@ data preparation, model building, and model evaluation by using the utility func
 | [dkn](dkn_synthetic.ipynb) | Synthetic Data | Python CPU, GPU | Utilizing the Deep Knowledge-Aware Network (DKN) [2] algorithm for news recommendations using information from a knowledge graph, in a Python+GPU (TensorFlow) environment.
 | [fastai](fastai_movielens.ipynb) | MovieLens | Python CPU, GPU | Utilizing FastAI recommender to predict movie ratings in a Python+GPU (PyTorch) environment.
 | [lightgbm](lightgbm_tinycriteo.ipynb) | Criteo | Python CPU | Utilizing LightGBM Boosting Tree to predict whether or not a user has clicked on an e-commerce ad |
+| [lstur](lstur_synthetic.ipynb) | Synthetic Data | Python CPU, GPU | Utilizing the Neural News Recommendation with Long- and Short-term User Representations (LSTUR) [9] for news recommendation, in a Python+GPU (Tensorflow) enviroment.
+| [naml](naml_synthetic.ipynb) | Synthetic Data | Python CPU, GPU | Utilizing the Neural News Recommendation with Attentive Multi-View Learning (NAML) [7] to algorithm for news recommendation using news verticle, subverticle, title and body information, in a Python+GPU (Tensorflow) environment.  
 | [ncf](ncf_movielens.ipynb) | MovieLens | Python CPU, GPU |  Utilizing Neural Collaborative Filtering (NCF) [1] to predict movie ratings in a Python+GPU (TensorFlow) environment.
+| [npa](npa_synthetic.ipynb) | Synthetic Data | Python CPU, GPU | Utilizing the Neural News Recommendation with Personalized Attention (NPA) [10] for news recommendation, in a Python+GPU (Tensorflow) environment.  
+| [nrms](nrms_synthetic.ipynb) | Synthetic Data | Python CPU, GPU | Utilizing the Neural News Recommendation with Multi-Head Self-Attention (NRMS) [8] for news recommendation, in a Python+GPU (Tensorflow) environment.  
 | [rbm](rbm_movielens.ipynb)| MovieLens | Python CPU, GPU | Utilizing the Restricted Boltzmann Machine (rbm) [4] to predict movie ratings in a Python+GPU (TensorFlow) environment.<br>
 | [rlrmc](rlrmc_movielens.ipynb) | Movielens | Python CPU | Utilizing the Riemannian Low-rank Matrix Completion (RLRMC) [6] to predict movie rating in a Python+CPU environment
 | [sar](sar_movielens.ipynb) | MovieLens | Python CPU | Utilizing Simple Algorithm for Recommendation (SAR) algorithm to predict movie ratings in a Python+CPU environment.
@@ -23,6 +27,10 @@ data preparation, model building, and model evaluation by using the utility func
 [2] _DKN: Deep Knowledge-Aware Network for News Recommendation_, Hongwei Wang, Fuzheng Zhang, Xing Xie and Minyi Guo. WWW 2018.<br>
 [3] _xDeepFM: Combining Explicit and Implicit Feature Interactions for Recommender Systems_, Jianxun Lian, Xiaohuan Zhou, Fuzheng Zhang, Zhongxia Chen, Xing Xie and Guangzhong Sun. KDD 2018.<br>
 [4] _Restricted Boltzmann Machines for Collaborative Filtering_, Ruslan Salakhutdinov, Andriy Mnih and Geoffrey Hinton. ICML 2007.<br>
-[5] _Wide & Deep Learning for Recommender Systems_, Heng-Tze Cheng et al., arXiv:1606.07792 2016.
-[6] _A unified framework for structured low-rank matrix learning_, Pratik Jawanpuria and Bamdev Mishra, In International Conference on Machine Learning, 2018.
+[5] _Wide & Deep Learning for Recommender Systems_, Heng-Tze Cheng et al., arXiv:1606.07792 2016. <br>
+[6] _A unified framework for structured low-rank matrix learning_, Pratik Jawanpuria and Bamdev Mishra, In International Conference on Machine Learning, 2018. <br>
+[7] _NAML: Neural News Recommendation with Attentive Multi-View Learning_, Chuhan Wu, Fangzhao Wu, Mingxiao An, Jianqiang Huang, Yongfeng Huang and Xing Xie. IJCAI 2019.<br>
+[8] _NRMS: Neural News Recommendation with Multi-Head Self-Attention_, Chuhan Wu, Fangzhao Wu, Suyu Ge, Tao Qi, Yongfeng Huang, Xing Xie. in Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP).<br>
+[9] _LSTUR: Neural News Recommendation with Long- and Short-term User Representations_, Mingxiao An, Fangzhao Wu, Chuhan Wu, Kun Zhang, Zheng Liu and Xing Xie. ACL 2019.<br>
+[10] _NPA: Neural News Recommendation with Personalized Attention_, Chuhan Wu, Fangzhao Wu, Mingxiao An, Jianqiang Huang, Yongfeng Huang and Xing Xie. KDD 2019, ADS track.<br>