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@@ -77,6 +77,7 @@ The table below lists the recommender algorithms currently available in the repo
 | LightFM/Hybrid Matrix Factorization | [Python CPU](examples/02_model_hybrid/lightfm_deep_dive.ipynb) | Hybrid | Hybrid matrix factorization algorithm for both implicit and explicit feedbacks |
 | LightGBM/Gradient Boosting Tree<sup>*</sup> | [Python CPU](examples/00_quick_start/lightgbm_tinycriteo.ipynb) / [PySpark](examples/02_model_content_based_filtering/mmlspark_lightgbm_criteo.ipynb) | Content-Based Filtering | Gradient Boosting Tree algorithm for fast training and low memory usage in content-based problems |
 | LightGCN | [Python CPU / Python GPU](examples/02_model_collaborative_filtering/lightgcn_deep_dive.ipynb) | Collaborative Filtering | Deep learning algorithm with simplifies the design of GCN for predicting implicit feedback |
+| GeoIMC | [Python CPU](examples/00_quick_start/geoimc_movielens.ipynb) | Hybrid | Matrix completion algorithm that has into account user and item features using Riemannian conjugate gradients optimization and following a geometric approach. |
 | GRU4Rec | [Python CPU / Python GPU](examples/00_quick_start/sequential_recsys_amazondataset.ipynb) | Collaborative Filtering | Sequential-based algorithm that aims to capture both long and short-term user preferences using recurrent neural networks |
 | Neural Recommendation with Long- and Short-term User Representations (LSTUR)<sup>*</sup> | [Python CPU / Python GPU](examples/00_quick_start/lstur_MIND.ipynb) | Content-Based Filtering | Neural recommendation algorithm with long- and short-term user interest modeling |
 | Neural Recommendation with Attentive Multi-View Learning (NAML)<sup>*</sup> | [Python CPU / Python GPU](examples/00_quick_start/naml_MIND.ipynb) | Content-Based Filtering | Neural recommendation algorithm with attentive multi-view learning |