refact2

README.md

@@ -113,21 +113,20 @@ The table below lists the recommender algorithms currently available in the repo
 | Neural Recommendation with Long- and Short-term User Representations (LSTUR)<sup>*</sup> | Content-Based Filtering | Neural recommendation algorithm for recommending news articles with long- and short-term user interest modeling. It works in the CPU/GPU enviroment. | [Quick start](examples/00_quick_start/lstur_MIND.ipynb) |
 | Neural Recommendation with Attentive Multi-View Learning (NAML)<sup>*</sup> | Content-Based Filtering | Neural recommendation algorithm for recommending news articles with attentive multi-view learning. It works in the CPU/GPU enviroment. | [Quick start](examples/00_quick_start/naml_MIND.ipynb) |
 | Neural Collaborative Filtering (NCF) | Collaborative Filtering | Deep learning algorithm with enhanced performance for user/item implicit feedback. It works in the CPU/GPU enviroment.| [Quick start](examples/00_quick_start/ncf_movielens.ipynb) |
-| Neural Recommendation with Personalized Attention (NPA)<sup>*</sup> | Content-Based Filtering | Neural recommendation algorithm for recommending news articles with personalized attention network. It works in the CPU/GPU enviroment. |[Quick start](examples/00_quick_start/npa_MIND.ipynb) |
-| Neural Recommendation with Multi-Head Self-Attention (NRMS)<sup>*</sup> | Content-Based Filtering | Neural recommendation algorithm for recommending news articles with multi-head self-attention. It works in the CPU/GPU enviroment. |  [Quick start](examples/00_quick_start/nrms_MIND.ipynb) |
+| Neural Recommendation with Personalized Attention (NPA)<sup>*</sup> | Content-Based Filtering | Neural recommendation algorithm for recommending news articles with personalized attention network. It works in the CPU/GPU enviroment. | [Quick start](examples/00_quick_start/npa_MIND.ipynb) |
+| Neural Recommendation with Multi-Head Self-Attention (NRMS)<sup>*</sup> | Content-Based Filtering | Neural recommendation algorithm for recommending news articles with multi-head self-attention. It works in the CPU/GPU enviroment. | [Quick start](examples/00_quick_start/nrms_MIND.ipynb) |
 | Next Item Recommendation (NextItNet) | Collaborative Filtering | Algorithm based on dilated convolutions and residual network that aims to capture sequential patterns. It considers both user/item interactions and features.  It works in the CPU/GPU enviroment. | [Quick start](examples/00_quick_start/sequential_recsys_amazondataset.ipynb) |
 | Restricted Boltzmann Machines (RBM) | Collaborative Filtering | Neural network based algorithm for learning the underlying probability distribution for explicit or implicit user/item feedback. It works in the CPU/GPU enviroment. | [Quick start](examples/00_quick_start/rbm_movielens.ipynb) / [Deep dive](examples/02_model_collaborative_filtering/rbm_deep_dive.ipynb) |
 | Riemannian Low-rank Matrix Completion (RLRMC)<sup>*</sup> | Collaborative Filtering | Matrix factorization algorithm using Riemannian conjugate gradients optimization with small memory consumption to predice user/item interactions. It works in the CPU enviroment. | [Quick start](examples/00_quick_start/rlrmc_movielens.ipynb) |
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-| Simple Algorithm for Recommendation (SAR)<sup>*</sup> | Collaborative Filtering | Similarity-based algorithm for implicit user/item feedback. | [Quick start](examples/00_quick_start/sar_movielens.ipynb) / [Deep dive](examples/02_model_collaborative_filtering/sar_deep_dive.ipynb) |
-| Short-term and Long-term Preference Integrated Recommender (SLi-Rec)<sup>*</sup> | Collaborative Filtering | Sequential-based algorithm that aims to capture both long and short-term user preferences using attention mechanism, a time-aware controller and a content-aware controller |  [Quick start](examples/00_quick_start/sequential_recsys_amazondataset.ipynb) |
-| Multi-Interest-Aware Sequential User Modeling (SUM)<sup>*</sup> | Collaborative Filtering | An enhanced memory network-based sequential user model which aims to capture users' multiple interests. | [Quick start](examples/00_quick_start/sequential_recsys_amazondataset.ipynb) |
-| Standard VAE | Collaborative Filtering | Generative Model for predicting user/item interactions. | [Deep dive](examples/02_model_collaborative_filtering/standard_vae_deep_dive.ipynb) |
-| Surprise/Singular Value Decomposition (SVD) | Collaborative Filtering | Matrix factorization algorithm for predicting explicit rating feedback in datasets that are not very large | [Deep dive](examples/02_model_collaborative_filtering/surprise_svd_deep_dive.ipynb) |
-| Term Frequency - Inverse Document Frequency (TF-IDF) | Content-Based Filtering | Simple similarity-based algorithm for content-based recommendations with text datasets | [Quick staert](examples/00_quick_start/tfidf_covid.ipynb) |
+| Simple Algorithm for Recommendation (SAR)<sup>*</sup> | Collaborative Filtering | Similarity-based algorithm for implicit user/item feedback.  It works in the CPU environment. | [Quick start](examples/00_quick_start/sar_movielens.ipynb) / [Deep dive](examples/02_model_collaborative_filtering/sar_deep_dive.ipynb) |
+| Short-term and Long-term Preference Integrated Recommender (SLi-Rec)<sup>*</sup> | Collaborative Filtering | Sequential-based algorithm that aims to capture both long and short-term user preferences using attention mechanism, a time-aware controller and a content-aware controller. It works in the CPU/GPU environment. | [Quick start](examples/00_quick_start/sequential_recsys_amazondataset.ipynb) |
+| Multi-Interest-Aware Sequential User Modeling (SUM)<sup>*</sup> | Collaborative Filtering | An enhanced memory network-based sequential user model which aims to capture users' multiple interests. It works in the CPU/GPU environment. | [Quick start](examples/00_quick_start/sequential_recsys_amazondataset.ipynb) |
+| Standard VAE | Collaborative Filtering | Generative Model for predicting user/item interactions.  It works in the CPU/GPU environment. | [Deep dive](examples/02_model_collaborative_filtering/standard_vae_deep_dive.ipynb) |
+| Surprise/Singular Value Decomposition (SVD) | Collaborative Filtering | Matrix factorization algorithm for predicting explicit rating feedback in small datasets. It works in the CPU/GPU environment. | [Deep dive](examples/02_model_collaborative_filtering/surprise_svd_deep_dive.ipynb) |
+| Term Frequency - Inverse Document Frequency (TF-IDF) | Content-Based Filtering | Simple similarity-based algorithm for content-based recommendations with text datasets. It works in the CPU environment. | [Quick staert](examples/00_quick_start/tfidf_covid.ipynb) |
 | Vowpal Wabbit (VW)<sup>*</sup> | Content-Based Filtering | Fast online learning algorithms, great for scenarios where user features / context are constantly changing. It uses the CPU for online learning. | [Deep dive](examples/02_model_content_based_filtering/vowpal_wabbit_deep_dive.ipynb) |
-| Wide and Deep | Deep learning algorithm that can memorize feature interactions and generalize user features | [Quick start](examples/00_quick_start/wide_deep_movielens.ipynb) | Hybrid |
-| xLearn/Factorization Machine (FM) & Field-Aware FM (FFM) | Hybrid | Quick and memory efficient algorithm to predict labels with user/item features | [Deep dive](examples/02_model_hybrid/fm_deep_dive.ipynb) |
+| Wide and Deep | Hybrid | Deep learning algorithm that can memorize feature interactions and generalize user features. It works in the CPU/GPU environment. | [Quick start](examples/00_quick_start/wide_deep_movielens.ipynb) |
+| xLearn/Factorization Machine (FM) & Field-Aware FM (FFM) | Hybrid | Quick and memory efficient algorithm to predict labels with user/item features. It works in the CPU/GPU environment. | [Deep dive](examples/02_model_hybrid/fm_deep_dive.ipynb) |
 
 **NOTE**: <sup>*</sup> indicates algorithms invented/contributed by Microsoft.