Fix typo.

PiperOrigin-RevId: 326825957

official/recommendation/README.md

@@ -4,7 +4,7 @@ This is an implementation of the Neural Collaborative Filtering (NCF) framework
 
 NCF is a general framework for collaborative filtering of recommendations in which a neural network architecture is used to model user-item interactions. Unlike traditional models, NCF does not resort to Matrix Factorization (MF) with an inner product on latent features of users and items. It replaces the inner product with a multi-layer perceptron that can learn an arbitrary function from data.
 
-Two instantiations of NCF are Generalized Matrix Factorization (GMF) and Multi-Layer Perceptron (MLP). GMF applies a linear kernel to model the latent feature interactions, and and MLP uses a nonlinear kernel to learn the interaction function from data. NeuMF is a fused model of GMF and MLP to better model the complex user-item interactions, and unifies the strengths of linearity of MF and non-linearity of MLP for modeling the user-item latent structures. NeuMF allows GMF and MLP to learn separate embeddings, and combines the two models by concatenating their last hidden layer. [neumf_model.py](neumf_model.py) defines the architecture details.
+Two instantiations of NCF are Generalized Matrix Factorization (GMF) and Multi-Layer Perceptron (MLP). GMF applies a linear kernel to model the latent feature interactions, and MLP uses a nonlinear kernel to learn the interaction function from data. NeuMF is a fused model of GMF and MLP to better model the complex user-item interactions, and unifies the strengths of linearity of MF and non-linearity of MLP for modeling the user-item latent structures. NeuMF allows GMF and MLP to learn separate embeddings, and combines the two models by concatenating their last hidden layer. [neumf_model.py](neumf_model.py) defines the architecture details.
 
 Some abbreviations used the code base include:
   - NCF: Neural Collaborative Filtering