item_item_collaborative _filtering_3.py

#------------------------
# USER-ITEM CALCULATIONS
#------------------------

user = 5985 # The id of the user for whom we want to generate recommendations
user_index = data[data.user == user].index.tolist()[0] # Get the frame index

# Get the artists the user has likd.
known_user_likes = data_items.ix[user_index]
known_user_likes = known_user_likes[known_user_likes >0].index.values

# Users likes for all items as a sparse vector.
user_rating_vector = data_items.ix[user_index]

# Calculate the score.
score = data_matrix.dot(user_rating_vector).div(data_matrix.sum(axis=1))

# Remove the known likes from the recommendation.
score = score.drop(known_user_likes)

# Print the known likes and the top 20 recommendations.
print known_user_likes
print score.nlargest(20)