Popularity Bias Share

Problem

Compute the popularity bias share: the fraction of recommended item ids that appear in the popular item list. Empty recommendations return 0.0.

Hint

High popularity share can starve the long tail of exposure.

case 1

in: [[1,2,3,4],[1,3]]

expected: 0.5

case 2

in: [[5,6],[1,2]]

expected: 0

case 3

in: [[1,1,1],[1]]

expected: 1

case 4

in: [[],[1]]

expected: 0

Starter codePython

def popularity_bias_share(recommended_ids, popular_ids):
    # Your code here
    pass

4 test cases · run your code against them in the editor