
    Design a function for an event recommendation system that calculates the total relevance score of suggested events for a user. Starting with an initial pool of n potential event matches, the system filters out pairs of less relevant events in each iteration, keeping only the most relevant ones. For each iteration, the relevance contribution is equal to the number of remaining events, and this process continues until no events remain for consideration. The function should sum up these relevance contributions across all iterations to determine the overall recommendation strength. For example, with 7 initial events, the system would calculate 7 + 5 + 3 + 1 to represent the decreasing pools of event suggestions and their combined relevance impact.
    

    
def sum_series(n):
    """
    Design a function for an event recommendation system that calculates the total relevance score of suggested events for a user. Starting with an initial pool of n potential event matches, the system filters out pairs of less relevant events in each iteration, keeping only the most relevant ones. For each iteration, the relevance contribution is equal to the number of remaining events, and this process continues until no events remain for consideration. The function should sum up these relevance contributions across all iterations to determine the overall recommendation strength. For example, with 7 initial events, the system would calculate 7 + 5 + 3 + 1 to represent the decreasing pools of event suggestions and their combined relevance impact.
    

    """
def sum_series(n):
    """
    Design a function for an event recommendation system that calculates the total relevance score of suggested events for a user. Starting with an initial pool of n potential event matches, the system filters out pairs of less relevant events in each iteration, keeping only the most relevant ones. For each iteration, the relevance contribution is equal to the number of remaining events, and this process continues until no events remain for consideration. The function should sum up these relevance contributions across all iterations to determine the overall recommendation strength. For example, with 7 initial events, the system would calculate 7 + 5 + 3 + 1 to represent the decreasing pools of event suggestions and their combined relevance impact.
    

    """
    if n < 1:
        return 0
    else:
        return n + sum_series(n - 2)