A very basic recommendation engine implemented in Python.

Usage: python recommender.py [-h|-f FILENAME|--user USER|--set-comparison SET_COMPARISON|--cutoff CUTOFF]

Running test suite: python test_recommender.py && mypy . && flake8 --max-line-length=100

Implementation thoughts:

• The entire recommendation algorithm can be found in compare_sets.py.
• I found it useful to plug different algorithms for computing the similarity of sets into the recommendation engine. The other comparison algorithms can be found in alternative_methods.py.
• In principle, nothing stops you from plugging custom objects as a similarity_matrix into the similar_users() and recommendations() functions. (I don't supply an example for this though.) You might want to do that because the similarity_matrix I calculate grows is O(N^2) in the number of users - in real world settings you'd want to optimize this.
• You have to decide on a numeric value for the similarity of two sets of liked products to consider two users similar in taste. I plugged that value as parameter 'cutoff' into the relevant functions.
• I am not sure the MinHash-algorithm I used is stable - I sort of whipped it up. It is hard to test stochastic algorithms, I considered this outside the scope of this exercise.