From 882777281c991b3be001098b8a91d601b5aadd0e Mon Sep 17 00:00:00 2001
From: Kingston257 <63620204+Kingston257@users.noreply.github.com>
Date: Mon, 10 Jun 2024 13:15:14 +0100
Subject: [PATCH] Fix typo in README.md

Made the following grammatical corrections to line 97 of the file:
- "has into account" should be "takes into account".
- "Riemannian conjugate gradients optimization" should be "Riemannian conjugate gradient optimization" (singular form).
- "and following a geometric approach" should be "and follows a geometric approach".
---
 README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/README.md b/README.md
index 8b0a3015d..74805200d 100644
--- a/README.md
+++ b/README.md
@@ -94,7 +94,7 @@ The table below lists the recommendation algorithms currently available in the r
 | LightFM/Factorization Machine | Collaborative Filtering | Factorization Machine algorithm for both implicit and explicit feedbacks. It works in the CPU environment. | [Quick start](examples/02_model_collaborative_filtering/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 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 environment. | [Deep dive](examples/02_model_collaborative_filtering/lightgcn_deep_dive.ipynb) |
-| GeoIMC<sup>*</sup> | Collaborative Filtering | 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 environment. | [Quick start](examples/00_quick_start/geoimc_movielens.ipynb) |
+| GeoIMC<sup>*</sup> | Collaborative Filtering | Matrix completion algorithm that takes into account user and item features using Riemannian conjugate gradient optimization and follows a geometric approach. It works in the CPU environment. | [Quick start](examples/00_quick_start/geoimc_movielens.ipynb) |
 | GRU | 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 environment. | [Quick start](examples/00_quick_start/sequential_recsys_amazondataset.ipynb) |
 | Multinomial VAE | Collaborative Filtering | Generative model for predicting user/item interactions. It works in the CPU/GPU environment. | [Deep dive](examples/02_model_collaborative_filtering/multi_vae_deep_dive.ipynb) |
 | Neural Recommendation with Long- and Short-term User Representations (LSTUR)<sup>*</sup> | Content-Based Filtering | Neural recommendation algorithm for recommending news articles with long- and short-term user interest modeling. It works in the CPU/GPU environment. | [Quick start](examples/00_quick_start/lstur_MIND.ipynb) |