Merge pull request #937 from tqtg/tqtg/cornac_bpr_tutorial

Add Cornac BPR deep dive notebook

README.md

@@ -50,6 +50,7 @@ The table below lists the recommender algorithms currently available in the repo
 | Algorithm | Environment | Type | Description | 
 | --- | --- | --- | --- |
 | Alternating Least Squares (ALS) | [PySpark](notebooks/00_quick_start/als_movielens.ipynb) | Collaborative Filtering | Matrix factorization algorithm for explicit or implicit feedback in large datasets, optimized by Spark MLLib for scalability and distributed computing capability | 
+| Cornac/Bayesian Personalized Ranking (BPR) | [Python CPU](notebooks/02_model/cornac_bpr_deep_dive.ipynb) | Collaborative Filtering | Matrix factorization algorithm for predicting item ranking with implicit feedback | 
 | Deep Knowledge-Aware Network (DKN)<sup>*</sup> | [Python CPU / Python GPU](notebooks/00_quick_start/dkn_synthetic.ipynb) | Content-Based Filtering | Deep learning algorithm incorporating a knowledge graph and article embeddings to provide powerful news or article recommendations | 
 | Extreme Deep Factorization Machine (xDeepFM)<sup>*</sup> | [Python CPU / Python GPU](notebooks/00_quick_start/xdeepfm_criteo.ipynb) | Hybrid | Deep learning based algorithm for implicit and explicit feedback 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 |

notebooks/02_model/README.md

@@ -11,6 +11,7 @@ In this directory, notebooks are provided to give a deep dive into training mode
 | [baseline_deep_dive](baseline_deep_dive.ipynb) | --- | Deep dive on baseline performance estimation.
 | [ncf_deep_dive](ncf_deep_dive.ipynb) | Python CPU, GPU | Deep dive on a NCF algorithm and implementation.
 | [rbm_deep_dive](rbm_deep_dive.ipynb)| Python CPU, GPU | Deep dive on the rbm algorithm and its implementation.
+| [cornac_bpr_deep_dive](cornac_bpr_deep_dive.ipynb) | Python CPU | Deep dive on the BPR algorithm and implementation.
 | [sar_deep_dive](sar_deep_dive.ipynb) | Python CPU | Deep dive on the SAR algorithm and implementation.
 | [surprise_svd_deep_dive](surprise_svd_deep_dive.ipynb) | Python CPU | Deep dive on a SVD algorithm and implementation.
 | [vowpal_wabbit_deep_dive](vowpal_wabbit_deep_dive.ipynb) | Python CPU | Deep dive into using Vowpal Wabbit for regression and matrix factorization.