#字典打分
critics={
'Lisa Rose':{
'Lady in the Water':2.5,
'Snakes on a Plane':3.5,
'Just My Luck':3.0,
'Superman Returns':3.5,
'You,Me and Dupree':2.5,
'The Night Listener':3.0
},
'Gene Seymour':{
'Lady in the Water':3.0,
'Snakes on a Plane':3.5,
'Just My Luck':1.5,
'Superman Returns':5.0,
'The Night Listener':3.0,
'You,Me and Dupree':3.5
},
'Michael Phillips':{
'Lady in the Water':2.5,
'Snakes on a Plane':3.0,
'Superman Returns':3.5,
'The Night Listener':4.0
},
'Claudia Puig':{
'Snakes on a Plane':3.5,
'Just My Luck':3.0,
'The Night Listener':4.5,
'Superman Returns':4.0,
'You,Me and Dupree':2.5,
},
'Mick LaSalle':{
'Lady in the Water':3.0,
'Snakes on a Plane':4.0,
'Just My Luck':2.0,
'Superman Returns':3.0,
'The Night Listener':3.0,
'You,Me and Dupree':2.0
},
'Jack Matthews':{
'Lady in the Water':3.0,
'Snakes on a Plane':4.0,
'The Night Listener':3.0,
'Superman Returns':5.0,
'You,Me and Dupree':3.5
},
'Toby':{
'Snakes on a Plane':4.5,
'You,Me and Dupree':1.0,
'Superman Returns':4.0
}
}
from math import sqrt
#欧几里得距离相关度
def sim_distance(prefs,person1,person2):
si={}
for item in prefs[person1]:
if item in prefs[person2]:
si[item] = 1
#如果两者没有共同之处，则返回0
if len(si) == 0:
return 0
#计算所有差值的平方和
sum_of_squares = sum([pow(prefs[person1][item]-prefs[person2][item],2)
for item in prefs[person1] if item in prefs[person2]])
return 1/(1+sqrt(sum_of_squares))
#皮尔逊系相关系数
def sim_pearson(prefs,p1,p2):
si = {}
for item in prefs[p1]:
if item in prefs[p2]:
si[item] = 1
n= len(si)
if n==0: return 1
sum1 = sum([prefs[p1][it] for it in si])
sum2 = sum([prefs[p2][it] for it in si])
sum1Sq = sum([pow(prefs[p1][it],2) for it in si])
sum2Sq = sum([pow(prefs[p2][it],2) for it in si])
pSum = sum([prefs[p1][it]*prefs[p2][it] for it in si])
#计算评价值
num = pSum-(sum1*sum2/n)
den = sqrt((sum1Sq-pow(sum1,2)/n)*(sum2Sq-pow(sum2,2)/n))
if den ==0: return 0
r = num/den
return r
def topMatches(prefs,person,n=5,similarity=sim_pearson):
scores=[(similarity(prefs,person,other),other)
for other in prefs if other!= person]
scores.sort()
scores.reverse()
return scores[0:n]
def getRecommendations(prefs,person,similarity=sim_pearson):
totals={}
simSums={}
for other in prefs:
if other == person:continue
sim=similarity(prefs,person,other)
if sim<=0:continue
for item in prefs[other]:
if item not in prefs[person] or prefs[person][item]==0:
#相似度*评价值
totals.setdefault(item,0)
totals[item]+=prefs[other][item]*sim
#相似度之和
simSums.setdefault(item,0)
simSums[item]+=sim
rankings=[(total/simSums[item],item) for item,total in totals.items()]
rankings.sort()
rankings.reverse()
return rankings
def transformPrefs(prefs):
result={}
for person in prefs:
for item in prefs[person]:
result.setdefault(item,{})
#将物品和人员对调
result[item][person]=prefs[person][item]
return result
def calculateSimilarItems(prefs,n=10):
result = {}
itemPrefs = transformPrefs(prefs)
c = 0
for item in itemPrefs:
c+=1
if c%100==0:
print ("%d /%d" % (c,len(lenPrefs)))
scores = topMatches(itemPrefs,item,n=n,similarity=sim_distance)
result[item]=scores
return result
def getRecommendedItems(prefs,itemMatch,user):
userRatings = prefs[user]
scores={}
totalSim={}
#循环遍历由当前用户评分的物品
for (item,rating) in userRatings.items():
for (similarity,item2) in itemMatch[item]:
if item2 in userRatings:continue
scores.setdefault(item2,0)
scores[item2] += similarity*rating
totalSim.setdefault(item2,0)
totalSim[item2] += similarity
rankings = [(score/totalSim[item],item) for item,score in scores.items()]
rankings.sort()
rankings.reverse()
return rankings
