
    Given a list of paired user interaction metrics where each pair contains message frequency and content similarity scores between two users, find the strongest potential friend connection by calculating the maximum absolute product of these interaction metrics. The interaction metrics can be positive or negative values, where negative values indicate opposing interaction patterns, and the absolute product represents the overall strength of the potential connection regardless of the pattern alignment.
    

    
def max_product_tuple(list1):
    """
    Given a list of paired user interaction metrics where each pair contains message frequency and content similarity scores between two users, find the strongest potential friend connection by calculating the maximum absolute product of these interaction metrics. The interaction metrics can be positive or negative values, where negative values indicate opposing interaction patterns, and the absolute product represents the overall strength of the potential connection regardless of the pattern alignment.
    

    """
def max_product_tuple(list1):
    """
    Given a list of paired user interaction metrics where each pair contains message frequency and content similarity scores between two users, find the strongest potential friend connection by calculating the maximum absolute product of these interaction metrics. The interaction metrics can be positive or negative values, where negative values indicate opposing interaction patterns, and the absolute product represents the overall strength of the potential connection regardless of the pattern alignment.
    

    """
    result_max = max([abs(x * y) for (x, y) in list1])
    return result_max