
    Given a list of paired user interaction metrics where each pair contains two users' mutual connection strength scores, find the strongest potential friend recommendation by calculating the maximum absolute product between any two users' connection scores. The connection strength scores can be positive or negative, where positive values indicate active engagement and negative values represent conflicting interests or minimal interaction.
    

    
def max_product_tuple(list1):
    """
    Given a list of paired user interaction metrics where each pair contains two users' mutual connection strength scores, find the strongest potential friend recommendation by calculating the maximum absolute product between any two users' connection scores. The connection strength scores can be positive or negative, where positive values indicate active engagement and negative values represent conflicting interests or minimal interaction.
    

    """
def max_product_tuple(list1):
    """
    Given a list of paired user interaction metrics where each pair contains two users' mutual connection strength scores, find the strongest potential friend recommendation by calculating the maximum absolute product between any two users' connection scores. The connection strength scores can be positive or negative, where positive values indicate active engagement and negative values represent conflicting interests or minimal interaction.
    

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