import math
from operator import itemgetter
data = {'A':{'a','b','d'}, 'B':{'b','c','e'}, 'C':{'c','d'}, 'D':{'b','c','d'}, 'E':{'a','d'}}
def ItemSimilarity(data):
#calculate co-rated users between itme
C = dict()
N = dict()
for u, items in data.items():
for i in items:
if i not in N:
N[i] = 1
else :
N[i] += 1
if i not in C:
C[i] = dict()
for j in items:
if i == j:
continue
if j not in C[i]:
C[i][j] = 1
else :
C[i][j] += 1
'''
for i , k in C.items():
for j, sim in k.items():
print i, j, sim
print
print '-----------'
'''
#calculate final similarity matrix W
W = dict()
for i, related_items in C.items():
W[i] = dict()
for j, cij in related_items.items():
W[i][j] = cij / math.sqrt(N[i] * N[j])
return W
Item_Simi = ItemSimilarity(data) #compute similarity between different items
for i, item in Item_Simi.items():
for j, Simi in sorted(item.items(), key = itemgetter(1), reverse = True):
print 'The similarity between ' + i + ' and ' + j + ' is ',
print Simi
def Recommendation(data, W, K):
rank = dict()
ru = data.keys()
rui = 1
for k in ru:#user k
rank[k] = dict()
for i in data[k]:#i is the items user k buyed
#print W[i]#the items buyed by k when k buyed i
for j, wj in sorted(W[i].items(), key = itemgetter(1), reverse = True)[0:K]:
#j is item ranked top K similarest with i buyed by k
if j not in rank[k]:#when the interest user k see item j has never computed
rank[k][j] = rui * wj
else:
rank[k][j] += rui * wj
return rank
result = Recommendation(data,Item_Simi, 3)
for i, j_item in result.items():
for j, interest in sorted(j_item.items(), key = itemgetter(1), reverse = True):
print ' the interest ' + i + ' buy ' + j +' is ',
print interest