基于用户推荐产品
a，b用户之间相似度计算公式=(a,b两用户共同购买东西件数)/（a购买产品件数*b购买产品件数）½
from math import sqrt
user = dict()
user["A"] = {'wash closh','knife','dish'}
user["B"] = {'wash closh','flowers','cup'}
user["C"] = {'wash closh','dish'}
user["D"] = {'flowers','knife'}
# print user
def userSimilarity(users):
items_user = dict()
for u,item in users.items():
for i in item:
if i not in items_user:
items_user[i] =set()
items_user[i].add(u)
# print(items_user)
C=dict()
N=dict()
for i,users in items_user.items():
for u in users:
N.setdefault(u,0)
N[u]+=1
C.setdefault(u,{})
for v in users:
if u==v:
continue
C[u].setdefault(v,0)
C[u][v]+=1
#print('C is',C)
#print('N is',N)
W = dict()
for u,related_users in C.items():
W.setdefault(u,{})
for v,cuv in related_users.items():
W[u].setdefault(v,0)
W[u][v]+=cuv/sqrt(N[u]*N[v])
# print('W is',W)
return W
def recommend(user,trains,W):
rank = dict()
items = trains[user]
for u,wuv in W[user].items():
for i in trains[u]:
if i in items:
continue
rank.setdefault(i,0)
rank[i]+=wuv
return sorted(rank.items(),key = None,reverse = False)
# print userSimilarity(user)
list_NO = ["A","B","C","D"]
for i in list_NO:
print "user {} recomand: {}".format(i,recommend(i,user,userSimilarity(user)))
