item_user.setdefault(item, set())

This line of code creates a dictionary where each key is an item and the value is an empty set. If the key already exists in the dictionary, the value is not overwritten. The purpose of this line is to initialize the dictionary with all items and empty sets as values, so that later on, we can add users who have interacted with each item to the corresponding set.

相关问题

for item in results: user, movie, rating = item.uid, item.iid, item.rate self.trainSet.setdefault(user, {}) self.trainSet[user][movie] = rating trainSet_len += 1 self.testSet.setdefault(user, {}) self.testSet[user][movie] = rating testSet_len += 1解释·代码

这段代码是一个协同过滤算法中的一部分，用于将推荐系统所得到的用户-物品评分矩阵按照一定比例分成训练集和测试集。首先，程序遍历推荐系统得到的评分数据，将每个评分记录的用户、物品和评分值分别赋值给变量user、movie、rating。然后，程序通过给定的训练集比例将该评分记录加入训练集中。当前代码中将所有的记录都放入训练集中，因此训练集大小为1。同时，程序也将该记录加入测试集中，测试集大小同样为1。最终，程序会得到一个训练集和一个测试集，用于协同过滤算法中的推荐任务。

给每一行代码增加注释：class TopNRecommend(): #初始化数据结构为[(用户id，新闻id，评分)] def init(self, data): data_dic = {} for line in data: if not line[0] in data_dic.keys(): data_dic[line[0]] = {line[1]: line[2]} else: data_dic[line[0]][line[1]] = line[2] self.data = data_dic self.ItemSimilarity() #计算相似的数据 def ItemSimilarity(self): self.itemSim = dict() movie_popular = dict() # item_user_count{item: likeCount} the number of users who like the item count = dict() # count{i:{j:value}} the number of users who both like item i and j # 计算每部新闻的相关度 # 先计算每件物品被喜欢的人次、物品关系矩阵C及相似度矩阵W，代码中分别为movie_popular，及过程中的itemSim和最终的itemSim。 #计算每个新闻的观看次数 for user, musics in self.data.items(): for movie in musics: if movie not in movie_popular: movie_popular[movie] = 0 movie_popular[movie] += 1 movie_count = len(movie_popular) print('Total musics: %d' % movie_count) print("musics count is:{}", movie_popular) # 计算物品关系矩阵 for user, musics in self.data.items(): for m1 in musics: for m2 in musics: if m1 == m2: continue self.itemSim.setdefault(m1, {}) self.itemSim[m1].setdefault(m2, 0) self.itemSim[m1][m2] += 1 / math.log(1 + len(musics)) print('Build co-rated users matrix success!')

#定义一个名为TopNRecommend的类
class TopNRecommend():
#初始化数据结构为[(用户id，新闻id，评分)]
def __init__(self, data):
data_dic = {}
#将数据转化为字典类型，键为用户id，值为该用户对新闻的评分
for line in data:
if not line[0] in data_dic.keys():
data_dic[line[0]] = {line[1]: line[2]}
else:
data_dic[line[0]][line[1]] = line[2]
self.data = data_dic
#计算每个新闻之间的相似性
self.ItemSimilarity()

#计算每个新闻之间的相似性
def ItemSimilarity(self):
#存储新闻之间的相似度
self.itemSim = dict()
#存储每个新闻的流行度
movie_popular = dict()
#存储每个新闻被喜欢的人次数
count = dict()

#计算每个新闻的观看次数
for user, musics in self.data.items():
for movie in musics:
if movie not in movie_popular:
movie_popular[movie] = 0
movie_popular[movie] += 1
movie_count = len(movie_popular)
print('Total musics: %d' % movie_count)
print("musics count is:{}", movie_popular)

#计算物品关系矩阵
for user, musics in self.data.items():
for m1 in musics:
for m2 in musics:
if m1 == m2:
continue
self.itemSim.setdefault(m1, {})
self.itemSim[m1].setdefault(m2, 0)
#计算物品关系矩阵中的每个元素的值
self.itemSim[m1][m2] += 1 / math.log(1 + len(musics))
print('Build co-rated users matrix success!')