performance_of_topN_recommendation

This is my implementation of [Paolo 2010: Performance of Recommender Algorithms on Top-N Recommendation Tasks]

how to use

###################
# setup

import numpy as np

# set simple radom recommendation model    
class random_model:
    def fit(self, user_ids, item_ids, values):
        # no training
        return self
    def predict(self, user_ids, item_ids):
        # predict randomly
        return [np.random.rand() for u in user_ids]

###################
# get hit=(0 or 1) from a test set (user_id, item_id) and a fitted model.
from performance_of_topN_recommendation import get_hit
    
fitted_model = random_model().fit(user_ids=[], item_ids=[], values=[])

user_id = '1'
item_id = '5'
all_item_ids = ['0','1','2','3','4','6','7','8','9']
topN=[2,3,4]
    
result = get_hit(fitted_model, user_id, item_id, all_item_ids, topN=topN) 

print(result)
# >{2: 0, 3: 1, 4: 1} # result of hit or not of each topN

###################
# get recall and precision from train and test dataset
from performance_of_topN_recommendation import get_hit_arrays

# set data
train_user_ids = np.random.choice(range(20), size=1000)
train_item_ids = np.random.choice(range(30), size=1000)
train_values   = np.random.choice(range(6),  size=1000)

test_user_ids = np.random.choice(range(20), size=500)
test_item_ids = np.random.choice(range(30), size=500)
test_values   = np.random.choice(range(6),  size=500)

# set simple model    
class random_model:
    def fit(self, user_ids, item_ids, values):
        return self
    def predict(self, user_ids, item_ids):
        return [np.random.rand() for u in user_ids]

model = random_model()        

result_dict = get_hit_arrays(
                    train_user_ids, train_item_ids, train_values
                   ,test_user_ids, test_item_ids, test_values
                   ,model
                   ,topN=[5,10,15]
                   )
print(result_dict)
""" > {
5: {'sum_of_hit': 20, 'recall': 0.23529411764705882, 'precision': 0.047058823529411764}, 
10: {'sum_of_hit': 32, 'recall': 0.3764705882352941, 'precision': 0.03764705882352941}, 
15: {'sum_of_hit': 40, 'recall': 0.47058823529411764, 'precision': 0.03137254901960784}
}
"""

Reference:

@inproceedings{cremonesi2010performance,
  title={Performance of recommender algorithms on top-n recommendation tasks},
  author={Cremonesi, Paolo and Koren, Yehuda and Turrin, Roberto},
  booktitle={Proceedings of the fourth ACM conference on Recommender systems},
  pages={39--46},
  year={2010},
  organization={ACM}
}