# Deep Matrix Factorization
# IMPLEMENTATION IN PROGRESS
class DeepMatrixFactorizationBase:
    """
    Deep Matrix Factorization (DMF) Base Implementation.

    A neural network-based approach that learns user and item embeddings through
    multiple non-linear transformations. Unlike traditional MF, DMF can capture
    complex interaction patterns through deep neural networks.

    Attributes:
        param1: First parameter description
        param2: Second parameter description
        user_layers (List[int]): Architecture of user tower layers
        item_layers (List[int]): Architecture of item tower layers
        activation (str): Activation function for hidden layers
        dropout_rate (float): Dropout rate for regularization

    Features:
        - Deep neural architecture for both user and item representations
        - Non-linear transformation layers
        - Interaction layer for user-item matching
        - Flexible architecture configuration
        - Dropout-based regularization

    Mathematical Formulation:
        The model learns representations through:
        1. User embedding: u = f_θ(W_u * x_u)
        2. Item embedding: i = f_θ(W_i * x_i)
        3. Matching score: y = g(u^T * i)

        Where:
        - f_θ represents non-linear transformations
        - W_u, W_i are learnable parameters
        - g is the final activation function

    References:
        Hong-Jian Xue, et al. "Deep Matrix Factorization Models for 
        Recommender Systems." IJCAI. 2017.
    """

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

        Args:
            param1: Description of first parameter
            param2: Description of second parameter

        Note:
            The model architecture should be configured based on the specific
            requirements of the recommendation task.
        """
        self.param1 = param1
        self.param2 = param2

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

        Implements the core DMF algorithm with deep neural networks.

        Args:
            None

        Returns:
            None

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