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README.md

@@ -92,7 +92,7 @@ For additional options to install the package (support for GPU, Spark etc.) see
 
 ## Algorithms
 
-The table below lists the recommender algorithms currently available in the repository. Notebooks are linked under the Environment column when different implementations are available.
+The table below lists the recommender algorithms currently available in the repository. Notebooks are linked under the Example column as Quick start, showcasing an easy to run example of the algorithm, or as Deep dive, explaining in detail the math and implementation of the algorithm.
 
 | Algorithm | Type | Description | Example |
 |-----------|------|-------------|---------|
@@ -101,11 +101,11 @@ The table below lists the recommender algorithms currently available in the repo
 | Cornac/Bayesian Personalized Ranking (BPR) | Collaborative Filtering | Matrix factorization algorithm for predicting item ranking with implicit feedback. It works in the CPU environment. | [Deep dive](examples/02_model_collaborative_filtering/cornac_bpr_deep_dive.ipynb) |
 | Cornac/Bilateral Variational Autoencoder (BiVAE) | Collaborative Filtering | Generative model for dyadic data (e.g., user-item interactions). It works in the CPU/GPU enviroment. | [Deep dive](examples/02_model_collaborative_filtering/cornac_bivae_deep_dive.ipynb) |
 | Convolutional Sequence Embedding Recommendation (Caser) | Collaborative Filtering | Algorithm based on convolutions that aim to capture both user’s general preferences and sequential patterns. It works in the CPU/GPU enviroment. | [Quick start](examples/00_quick_start/sequential_recsys_amazondataset.ipynb) |
-| Deep Knowledge-Aware Network (DKN)<sup>*</sup> | Content-Based Filtering | Deep learning algorithm incorporating a knowledge graph and article embeddings to provide powerful news or article recommendations. It works in the CPU/GPU enviroment. | [Quick start](examples/00_quick_start/dkn_MIND.ipynb) / [Deep dive](examples/02_model_content_based_filtering/dkn_deep_dive.ipynb) |
+| Deep Knowledge-Aware Network (DKN)<sup>*</sup> | Content-Based Filtering | Deep learning algorithm incorporating a knowledge graph and article embeddings for providing news or article recommendations. It works in the CPU/GPU enviroment. | [Quick start](examples/00_quick_start/dkn_MIND.ipynb) / [Deep dive](examples/02_model_content_based_filtering/dkn_deep_dive.ipynb) |
 | Extreme Deep Factorization Machine (xDeepFM)<sup>*</sup> | Hybrid | Deep learning based algorithm for implicit and explicit feedback with user/item features. It works in the CPU/GPU environment. | [Quick start](examples/00_quick_start/xdeepfm_criteo.ipynb) |
 | FastAI Embedding Dot Bias (FAST) | Collaborative Filtering | General purpose algorithm with embeddings and biases for users and items. It works in the CPU/GPU environment. | [Quick start](examples/00_quick_start/fastai_movielens.ipynb) |
 | LightFM/Hybrid Matrix Factorization | Hybrid | Hybrid matrix factorization algorithm for both implicit and explicit feedbacks. It works in the CPU environment. | [Quick start](examples/02_model_hybrid/lightfm_deep_dive.ipynb) |
-| LightGBM/Gradient Boosting Tree<sup>*</sup> | Content-Based Filtering | Gradient Boosting Tree algorithm for fast training and low memory usage in content-based problems. It works in CPU/GPU/PySpark environment. | [Quick start in CPU](examples/00_quick_start/lightgbm_tinycriteo.ipynb) / [Deep dive in PySpark](examples/02_model_content_based_filtering/mmlspark_lightgbm_criteo.ipynb) |
+| LightGBM/Gradient Boosting Tree<sup>*</sup> | Content-Based Filtering | Gradient Boosting Tree algorithm for fast training and low memory usage in content-based problems. It works in the CPU/GPU/PySpark environments. | [Quick start in CPU](examples/00_quick_start/lightgbm_tinycriteo.ipynb) / [Deep dive in PySpark](examples/02_model_content_based_filtering/mmlspark_lightgbm_criteo.ipynb) |
 | LightGCN | Collaborative Filtering | Deep learning algorithm which simplifies the design of GCN for predicting implicit feedback. It works in the CPU/GPU enviroment. | [Deep dive](examples/02_model_collaborative_filtering/lightgcn_deep_dive.ipynb) |
 | GeoIMC<sup>*</sup> | Hybrid | Matrix completion algorithm that has into account user and item features using Riemannian conjugate gradients optimization and following a geometric approach. It works in the CPU enviroment. | [Quick start](examples/00_quick_start/geoimc_movielens.ipynb) |
 | GRU4Rec | Collaborative Filtering | Sequential-based algorithm that aims to capture both long and short-term user preferences using recurrent neural networks. It works in the CPU/GPU enviroment. | [Quick start](examples/00_quick_start/sequential_recsys_amazondataset.ipynb) |