Merge pull request #1096 from cheetm/cheetm/add_lightfm_deepdive

cheetm/lightfm_deep_dive

README.md

@@ -77,6 +77,7 @@ The table below lists the recommender algorithms currently available in the repo
 | 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 |
 | Short-term and Long-term preference Integrated Recommender (SLi-Rec)<sup>*</sup> | [Python CPU / Python GPU](notebooks/00_quick_start/sequential_recsys_amazondataset.ipynb) | 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 |
 | Surprise/Singular Value Decomposition (SVD) | [Python CPU](notebooks/02_model/surprise_svd_deep_dive.ipynb) | Collaborative Filtering | Matrix factorization algorithm for predicting explicit rating feedback in datasets that are not very large |
+| LightFM/Hybrid Matrix Factorization | [Python CPU](notebooks/02_model/lightfm_deep_dive.ipynb) | Hybrid | Hybrid matrix factorization algorithm for both implicit and explicit feedbacks |
 | Term Frequency - Inverse Document Frequency (TF-IDF) | [Python CPU](notebooks/00_quick_start/tfidf_covid.ipynb) | Content-Based Filtering | Simple similarity-based algorithm for content-based recommendations with text datasets |
 | Vowpal Wabbit Family (VW)<sup>*</sup> | [Python CPU (online training)](notebooks/02_model/vowpal_wabbit_deep_dive.ipynb) | Content-Based Filtering | Fast online learning algorithms, great for scenarios where user features / context are constantly changing |
 | Wide and Deep | [Python CPU / Python GPU](notebooks/00_quick_start/wide_deep_movielens.ipynb) | Hybrid | Deep learning algorithm that can memorize feature interactions and generalize user features |

notebooks/02_model/README.md

@@ -16,5 +16,6 @@ In this directory, notebooks are provided to give a deep dive into training mode
 | [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.
 | [fm_deep_dive](fm_deep_dive.ipynb) | Python CPU | Deep dive into factorization machine (FM) and field-aware FM (FFM) algorithm.
+| [lightfm_deep_dive](lightfm_deep_dive.ipynb) | Python CPU | Deep dive into hybrid matrix factorisation model with LightFM.
 
 Details on model training are best found inside each notebook.