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Small clarification for jaccard and lift similarity measures #1668

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Mar 10, 2022
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Small clarification for jaccard and lift similarity measures #1668

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374550a
small clarification
miguelgfierro Mar 10, 2022
8fc4b48
:bug:
miguelgfierro Mar 10, 2022
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10 changes: 9 additions & 1 deletion recommenders/utils/python_utils.py
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Original file line number Diff line number Diff line change
Expand Up @@ -27,6 +27,11 @@ def exponential_decay(value, max_val, half_life):

def jaccard(cooccurrence):
"""Helper method to calculate the Jaccard similarity of a matrix of co-occurrences.
When comparing Jaccard with count co-occurrence and lift similarity, count favours
predictability, meaning that the most popular items will be recommended most of
the time. Lift, by contrast, favours discoverability/serendipity, meaning that an
item that is less popular overall but highly favoured by a small subset of users
is more likely to be recommended. Jaccard is a compromise between the two.

Args:
cooccurrence (numpy.ndarray): the symmetric matrix of co-occurrences of items.
Expand All @@ -46,7 +51,10 @@ def jaccard(cooccurrence):


def lift(cooccurrence):
"""Helper method to calculate the Lift of a matrix of co-occurrences.
"""Helper method to calculate the Lift of a matrix of co-occurrences. In comparison
with basic co-occurrence and Jaccard similarity, lift favours discoverability and
serendipity, as opposed to co-occurrence that favours the most popular items, and
Jaccard that is a compromise between the two.

Args:
cooccurrence (numpy.ndarray): The symmetric matrix of co-occurrences of items.
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