Exemplos de get_recommended_items em Python

Exemplo n.º 1

Arquivo: movielens.py Projeto: paudan/python-scripts

def main():
    prefs = loadMovieLens()
    itemsim = recommendations.calculate_similar_items(prefs, n=50)
    print (itemsim)
    print(recommendations.get_recommended_items(prefs, itemsim, '87')[0:30])

Exemplo n.º 2

Arquivo: ex4.py Projeto: damianofusco/Programming-Collective-Intelligence

def recommend_bookmarks(prefs, similar_table, user):
	return recommendations.get_recommended_items(prefs, similar_bookmarks, user)

# I'm getting an empty list from my function, but I don't know what I'm doing wrong.

Exemplo n.º 3

def recommend_bookmarks(prefs, similar_table, user):
    return recommendations.get_recommended_items(prefs, similar_bookmarks,
                                                 user)


# I'm getting an empty list from my function, but I don't know what I'm doing wrong.

Exemplo n.º 4

Arquivo: dataByTerm.py Projeto: otwm/CollectiveIntelligence

# -*- 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')

Exemplo n.º 5

Arquivo: test.py Projeto: otwm/CollectiveIntelligence

# -*- 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]

Exemplo n.º 6

Arquivo: main.py Projeto: rfguri/movie-recommender

# 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)