
    Design a function that determines the priority level of a content recommendation at position n in the social network's interaction sequence, where each value is derived from the strength of user connections based on their mutual interactions and shared network relationships. The sequence follows the Newman-Conway pattern where each new priority level is determined by the sum of values at positions equal to the previous two terms in the sequence, starting with P(1)=1 and P(2)=1. This priority level helps the recommendation engine determine the optimal order of content suggestions in a user's feed.
    

    
def sequence(n):
    """
    Design a function that determines the priority level of a content recommendation at position n in the social network's interaction sequence, where each value is derived from the strength of user connections based on their mutual interactions and shared network relationships. The sequence follows the Newman-Conway pattern where each new priority level is determined by the sum of values at positions equal to the previous two terms in the sequence, starting with P(1)=1 and P(2)=1. This priority level helps the recommendation engine determine the optimal order of content suggestions in a user's feed.
    

    """
def sequence(n):
    """
    Design a function that determines the priority level of a content recommendation at position n in the social network's interaction sequence, where each value is derived from the strength of user connections based on their mutual interactions and shared network relationships. The sequence follows the Newman-Conway pattern where each new priority level is determined by the sum of values at positions equal to the previous two terms in the sequence, starting with P(1)=1 and P(2)=1. This priority level helps the recommendation engine determine the optimal order of content suggestions in a user's feed.
    

    """
    if n == 1 or n == 2:
        return 1
    else:
        return sequence(sequence(n - 1)) + sequence(n - sequence(n - 1))