added emoji

README.md

@@ -101,11 +101,11 @@ The table below lists recommender models that are available in RecTools. See [Re
 
 | Model | Type | Description | Tutorials & Benchmarks |
 |----|----|---------|--------|
-| [implicit](https://github.com/benfred/implicit) ALS Wrapper | Matrix Factorization | `rectools.models.ImplicitALSWrapperModel` - Alternating Least Squares Matrix Factorizattion algorithm for implicit feedback. <br>*Supports: user/item features* | [Theory&Practice](https://rectools.readthedocs.io/en/latest/examples/tutorials/baselines_extended_tutorial.html#Implicit-ALS)<br><br>[50% boost to metrics with user/item features](examples/5_benchmark_iALS_with_features.ipynb) |
-| [implicit](https://github.com/benfred/implicit) ItemKNN Wrapper | Nearest Neighbours | `rectools.models.ImplicitItemKNNWrapperModel` - Algorithm that calculates item-item similarity matrix using distances between item vectors in user-item interactions matrix | [Theory&Practice](https://rectools.readthedocs.io/en/latest/examples/tutorials/baselines_extended_tutorial.html#ItemKNN) |
-| [LightFM](https://github.com/lyst/lightfm) Wrapper | Matrix Factorization | `rectools.models.LightFMWrapperModel` - Hybrid matrix factorization algorithm which utilises user and item features and supports a variety of losses.<br>*Supports: user/item features, warm/cold inference*  | [Theory&Practice](https://rectools.readthedocs.io/en/latest/examples/tutorials/baselines_extended_tutorial.html#LightFM)<br><br>[10-25 times faster inference with RecTools](examples/6_benchmark_lightfm_inference.ipynb)|
-| EASE | Linear Autoencoder | `rectools.models.EASEModel` - Embarassingly Shallow Autoencoders implementation that explicitly calculates dense item-item similarity matrix | [Theory&Practice](https://rectools.readthedocs.io/en/latest/examples/tutorials/baselines_extended_tutorial.html#EASE) |
-| PureSVD | Matrix Factorization | `rectools.models.PureSVDModel` - Truncated Singular Value Decomposition of user-item interactions matrix | [Theory&Practice](https://rectools.readthedocs.io/en/latest/examples/tutorials/baselines_extended_tutorial.html#PureSVD) |
+| [implicit](https://github.com/benfred/implicit) ALS Wrapper | Matrix Factorization | `rectools.models.ImplicitALSWrapperModel` - Alternating Least Squares Matrix Factorizattion algorithm for implicit feedback. <br>*Supports: user/item features* | 📙 [Theory & Practice](https://rectools.readthedocs.io/en/latest/examples/tutorials/baselines_extended_tutorial.html#Implicit-ALS)<br> 🚀 [50% boost to metrics with user/item features](examples/5_benchmark_iALS_with_features.ipynb) |
+| [implicit](https://github.com/benfred/implicit) ItemKNN Wrapper | Nearest Neighbours | `rectools.models.ImplicitItemKNNWrapperModel` - Algorithm that calculates item-item similarity matrix using distances between item vectors in user-item interactions matrix | 📙 [Theory & Practice](https://rectools.readthedocs.io/en/latest/examples/tutorials/baselines_extended_tutorial.html#ItemKNN) |
+| [LightFM](https://github.com/lyst/lightfm) Wrapper | Matrix Factorization | `rectools.models.LightFMWrapperModel` - Hybrid matrix factorization algorithm which utilises user and item features and supports a variety of losses.<br>*Supports: user/item features, warm/cold inference*  | 📙 [Theory & Practice](https://rectools.readthedocs.io/en/latest/examples/tutorials/baselines_extended_tutorial.html#LightFM)<br>🚀 [10-25 times faster inference with RecTools](examples/6_benchmark_lightfm_inference.ipynb)|
+| EASE | Linear Autoencoder | `rectools.models.EASEModel` - Embarassingly Shallow Autoencoders implementation that explicitly calculates dense item-item similarity matrix | 📙 [Theory & Practice](https://rectools.readthedocs.io/en/latest/examples/tutorials/baselines_extended_tutorial.html#EASE) |
+| PureSVD | Matrix Factorization | `rectools.models.PureSVDModel` - Truncated Singular Value Decomposition of user-item interactions matrix | 📙 [Theory & Practice](https://rectools.readthedocs.io/en/latest/examples/tutorials/baselines_extended_tutorial.html#PureSVD) |
 | DSSM | Neural Network | `rectools.models.DSSMModel` - Two-tower Neural model that learns user and item embeddings utilising their explicit features and learning on triplet loss.<br>*Supports: user/item features, warm inference*  | - |
 | Popular | Heuristic | `rectools.models.PopularModel` - Classic baseline which computes popularity of items.<br>Supports: cold inference | - |
 | Popular in Category | Heuristic |  `rectools.models.PopularInCategoryModel` - Model that computes poularity within category and applies mixing strategy to increase Diversity.<br>*Supports: cold inference*| - |