import os
os.environ["DJANGO_SETTINGS_MODULE"] = "movie.settings"
import django
django.setup()
from movie.models import *
from math import sqrt, pow
import operator
from django.db.models import Subquery, Q, Count
class UserCf:
def __init__(self, all_user):
self.all_user = all_user
def getItems(self, username1, username2):
return self.all_user[username1], self.all_user[username2]
def pearson(self, user1, user2):
sum_xy = 0.0
n = 0
sum_x = 0.0
sum_y = 0.0
sumX2 = 0.0
sumY2 = 0.0
for movie1, score1 in user1.items():
if movie1 in user2.keys():
n += 1
sum_xy += score1 * user2[movie1]
sum_x += score1
sum_y += user2[movie1]
sumX2 += pow(score1, 2)
sumY2 += pow(user2[movie1], 2)
if n == 0:
return 0
molecule = sum_xy - (sum_x * sum_y) / n
denominator = sqrt((sumX2 - pow(sum_x, 2) / n) * (sumY2 - pow(sum_y, 2) / n))
if denominator == 0:
return 0
r = molecule / denominator
return r
def nearest_user(self, current_user, n=1):
distances = {}
for user, rate_set in self.all_user.items():
if user != current_user:
distance = self.pearson(self.all_user[current_user], self.all_user[user])
distances[user] = distance
closest_distance = sorted(distances.items(), key=operator.itemgetter(1), reverse=True)
print("closest user:", closest_distance[:n])
return closest_distance[:n]
def recommend(self, username, n=3):
recommend = {}
nearest_user = self.nearest_user(username, n)
for user, score in dict(nearest_user).items():
for movies, scores in self.all_user[user].items():
if movies not in self.all_user[username].keys():
if movies not in recommend.keys():
recommend[movies] = scores * score
return sorted(recommend.items(), key=operator.itemgetter(1), reverse=True)
def recommend_by_user_id(user_id):
user_prefer = UserTagPrefer.objects.filter(user_id=user_id).order_by('-score').values_list('tag_id', flat=True)
current_user = User.objects.get(id=user_id)
if current_user.rate_set.count() == 0:
if len(user_prefer) != 0:
movie_list = Movie.objects.filter(tags__in=user_prefer)[:15]
else:
movie_list = Movie.objects.order_by("-num")[:15]
return movie_list
users_rate = Rate.objects.values('user').annotate(mark_num=Count('user')).order_by('-mark_num')
user_ids = [user_rate['user'] for user_rate in users_rate]
user_ids.append(user_id)
users = User.objects.filter(id__in=user_ids)
all_user = {}
for user in users:
rates = user.rate_set.all()
rate = {}
if rates:
for i in rates:
rate.setdefault(str(i.movie.id), i.mark)
all_user.setdefault(user.username, rate)
user_cf = UserCf(all_user=all_user)
recommend_list = [each[0] for each in user_cf.recommend(current_user.username, 15)]
movie_list = list(Movie.objects.filter(id__in=recommend_list).order_by("-num")[:15])
other_length = 15 - len(movie_list)
if other_length > 0:
fix_list = Movie.objects.filter(~Q(rate__user_id=user_id)).order_by('-collect')
for fix in fix_list:
if fix not in movie_list:
movie_list.append(fix)
if len(movie_list) >= 15:
break
return movie_list
def similarity(movie1_id, movie2_id):
movie1_set = Rate.objects.filter(movie_id=movie1_id)
movie1_sum = movie1_set.count()
movie2_sum = Rate.objects.filter(movie_id=movie2_id).count()
common = Rate.objects.filter(user_id__in=Subquery(movie1_set.values('user_id')), movie=movie2_id).values('user_id').count()
if movie1_sum == 0 or movie2_sum == 0:
return 0
similar_value = common / sqrt(movie1_sum * movie2_sum)
return similar_value
def recommend_by_item_id(user_id, k=15):
user_prefer = UserTagPrefer.objects.filter(user_id=user_id).order_by('-score').values_list('tag_id', flat=True)
user_prefer = list(user_prefer)[:3]
current_user = User.objects.get(id=user_id)
if current_user.rate_set.count() == 0:
if len(user_prefer) != 0:
movie_list = Movie.objects.filter(tags__in=user_prefer)[:15]
else:
movie_list = Movie.objects.order_by("-num")[:15]
print('from here')
return movie_list
un_watched = Movie.objects.filter(~Q(rate__user_id=user_id), tags__in=user_prefer).order_by('?')[:30]
watched = Rate.objects.filter(user_id=user_id).values_list('movie_id', 'mark')
distances = []
names = []
for un_watched_movie in un_watched:
for watched_movie in watched:
if un_watched_movie not in names:
names.append(un_watched_movie)
distances.append((similarity(un_watched_movie.id, watched_movie[0]) * watched_movie[1], un_watched_movie))
distances.sort(key=lambda x: x[0], reverse=True)
print('this is distances', distances[:15])
recommend_list = []
for mark, movie in distances:
if len(recommend_list) >= k:
break
if movie not in recommend_list:
recommend_list.append(movie)
print('recommend list', recommend_list)
return recommend_list
if __name__ == '__main__':
similarity(2003, 2008)
recommend_by_item_id(1)
