"""
Excercise 3
"""
import recommendations
similar_critics = recommendations.calculate_similar_items(recommendations.movies)
def get_precomputed_recommendedations(prefs, similar, user):
similar_prefs = {}
for other in similar:
similar_prefs[other] = prefs[other]
similar_prefs[user] = prefs[user]
return recommendations.get_recommendations(similar_prefs, user)
# The precompute recommendations tend to match up extremely well with the more
# laboriously computed recommendations. Probably because they are generated the
# same way. Larger datasets would probably make this match less exact, but
# would give huge speed improvements, since all algorithms tend to be fast for
# small n.
def main():
prefs = loadMovieLens()
itemsim = recommendations.calculate_similar_items(prefs, n=50)
print (itemsim)
print(recommendations.get_recommended_items(prefs, itemsim, '87')[0:30])
"""
Excercise 3
"""
import recommendations
similar_critics = recommendations.calculate_similar_items(
recommendations.movies)
def get_precomputed_recommendedations(prefs, similar, user):
similar_prefs = {}
for other in similar:
similar_prefs[other] = prefs[other]
similar_prefs[user] = prefs[user]
return recommendations.get_recommendations(similar_prefs, user)
# The precompute recommendations tend to match up extremely well with the more
# laboriously computed recommendations. Probably because they are generated the
# same way. Larger datasets would probably make this match less exact, but
# would give huge speed improvements, since all algorithms tend to be fast for
# small n.
# -*- coding: utf-8 -*- import recommendations reload(recommendations) info = recommendations.calculate_similar_items(recommendations.critics) # print info print recommendations.get_recommended_items(recommendations.critics, info, 'Jack Matthews')
# -*- coding: utf-8 -*-
import recommendations
reload(recommendations)
# from deliciousrec import *
# delusers = initialize_user_dict('programing')
# fill_items(delusers)
#
# print delusers
data = recommendations.load_movie_lens()
print '==================== 사용자 ===================='
print data['87']
print '==================== 추천 ===================='
print recommendations.getRecommendations(data, '87')[0:30]
print '==================== 항목 기반 ===================='
item_sim = recommendations.calculate_similar_items(data, rank=50)
print recommendations.get_recommended_items(data, item_sim, '87')[0:30]
# Lib imports
import recommendations, time
# Function's execution
t1 = time.time()
prefs=recommendations.load_movie_lens()
t2 = time.time()
print "Recomendations: {}".format(recommendations.get_recommendations(prefs,'87')[0:30])
t3 = time.time()
itemsim=recommendations.calculate_similar_items(prefs,50)
t4 = time.time()
print "Recommended items: {}".format(recommendations.get_recommended_items(prefs,itemsim,'87')[0:30])
t5 = time.time()
print "\nExecution times"
print "-----------------"
print "Load dataset: {} seconds".format(t2-t1)
print "User based filtering: {} seconds".format(t3-t2)
print "Calculate similar item: {} seconds".format(t4-t3)
print "Item based filtering: {} seconds".format(t5-t4)
