完善以下代码from random import seed,randrange # 模拟历史电影打分数据 seed(2050) data = {'user'+str(i):{'film'+str(randrange(1, 15)):randrange(1, 6) for j in range(randrange(3, 10))} for i in range(10)} #print(data) # 当前用户打分数据 user = {'film'+str(randrange(1, 15)):randrange(1,6) for i in range(5)} #print(user) # 基于用户的协同过滤算法原理: # 两个用户共同打分的电影最多,并且所有电影打分差值的平方和最小 f = lambda item:(-len(item[1].keys()&user), sum(((item[1].get(film)-user.get(film))**2 for film in user.keys()&item[1].keys()))) # 寻找最相似的用户及其对电影打分情况 similarUser, films = min(data.items(), key=f) #print(similarUser, films) print('known data'.center(50, '=')) print(data) print('current user'.center(50, '=')) print(user) print('most similar user and his films'.center(50, '=')) print(similarUser, films, sep=':') print('recommended film'.center(50, '=')) # 在当前用户没看过的电影中选择打分最高的进行推荐

以下是完善后的代码：

from random import seed, randrange

# 模拟历史电影打分数据
seed(2050)
data = {'user'+str(i):{'film'+str(randrange(1, 15)):randrange(1, 6) for j in range(randrange(3, 10))} for i in range(10)}

# 当前用户打分数据
user = {'film'+str(randrange(1, 15)):randrange(1,6) for i in range(5)}

# 基于用户的协同过滤算法原理:
# 两个用户共同打分的电影最多,并且所有电影打分差值的平方和最小
f = lambda item:(-len(item[1].keys()&user), sum(((item[1].get(film)-user.get(film))**2 for film in user.keys()&item[1].keys())))

# 寻找最相似的用户及其对电影打分情况
similarUser, films = min(data.items(), key=f)

print('known data'.center(50, '='))
print(data)

print('current user'.center(50, '='))
print(user)

print('most similar user and his films'.center(50, '='))
print(similarUser, films, sep=':')

print('recommended film'.center(50, '='))

# 在当前用户没看过的电影中选择打分最高的进行推荐
recommended_film = max(films.keys() - user.keys(), key=films.get)
print(recommended_film)

这段代码实现了基于用户的协同过滤算法，并在当前用户没看过的电影中选择打分最高的进行推荐。