def make_user_user_recommendations(self, user_id, num_recommendations=10):
'''
INPUT:
user_id - (int) a user id
num_recommendations - (int) the number of recommendations you want for the user
OUTPUT:
recs - (list) a list of recommendations for the user by article id
rec_names - (list) a list of recommendations for the user by article title
Description:
Loops through the users based on closeness to the input user_id
For each user - finds articles the user hasn't seen before and provides them as recs
Does this until m recommendations are found
Notes:
* Choose the users that have the most total article interactions
before choosing those with fewer article interactions.
* Choose articles with the articles with the most total interactions
before choosing those with fewer total interactions.
'''
recs = []
neighbors_df = self.get_top_sorted_users(user_id)
user_articles_id, user_articles_names = self.get_user_articles(user_id)
for neighbor in neighbors_df.index:
neighbor_articles_id, neighbor_articles_names = self.get_user_articles(neighbor)
sorted_neighbor_article_ids = Utils.get_top_articles_df(neighbor_articles_id, self.interactions_df)
sorted_neighbor_article_ids = sorted_neighbor_article_ids.index.values
article_not_read = np.setdiff1d(sorted_neighbor_article_ids, user_articles_id, assume_unique=True)
article_not_read = [str(i) for i in article_not_read]
recs = np.unique(np.concatenate([article_not_read, recs], axis=0))
if len(recs) >= num_recommendations:
break
if len(recs) >= num_recommendations:
recs = recs[:num_recommendations]
recommended_articles = Utils.get_article_names(recs, self.interactions_df, 'title')
return recommended_articles
def __init__(self, interactions_df):
self.interactions_df = interactions_df
self.article_ids = self.interactions_df['article_id'].unique()
self.top_articles_df = Utils.get_top_articles_df(self.article_ids, self.interactions_df)