# Neural Matrix Factorization
# IMPLEMENTATION IN PROGRESS
class NeuralMatrixFactorizationBase:
    """
    Neural Matrix Factorization (NeuMF) Base Implementation.

    A hybrid model that combines Matrix Factorization (MF) with Multi-Layer Perceptron (MLP)
    to learn non-linear user-item interactions through neural networks.

    Attributes:
        param1: First parameter description
        param2: Second parameter description
        embedding_size (int): Size of the embedding vectors for users and items
        layers (List[int]): Architecture of the MLP layers
        learning_rate (float): Learning rate for optimization
        regularization (float): L2 regularization term

    Features:
        - Dual embedding paths (MF path and MLP path)
        - Non-linear interaction modeling
        - Flexible neural architecture
        - Fusion of latent features

    References:
        He, X., Liao, L., Zhang, H., Nie, L., Hu, X., & Chua, T. S. (2017). 
        Neural collaborative filtering. In Proceedings of the 26th international 
        conference on world wide web (pp. 173-182).
    """

    def __init__(self, param1, param2):
        """
        Initialize the Neural Matrix Factorization model.

        Args:
            param1: Description of first parameter
            param2: Description of second parameter
        """
        self.param1 = param1
        self.param2 = param2

    def some_method(self):
        """
        Method description.

        Implements the core NeuMF algorithm combining MF and MLP paths.

        Args:
            None

        Returns:
            None

        Raises:
            NotImplementedError: Method needs to be implemented
        """
        pass