協同フィルタリングアルゴリズムに基づく推奨システム実装
2985 ワード
# -*- coding: cp936 -*-
#
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,'You,Me and Dupree':3.5,'The Night Listener':3.0},
'Michael Phillips':{'Lady in the Water':2.5,'Snakes on a Plane':3.0,'Just My Luck':3.0,
'Superman Returns':3.5,'You,Me and Dupree':2.5,'The Night Listener':4.0},
'Claudia Puig':{'Lady in the Water':2.5,'Snakes on a Plane':3.5,'Just My Luck':3.0,
'Superman Returns':4.0,'You,Me and Dupree':2.5,'The Night Listener':4.5},
'Toby':{'Snakes on a Plane':4.5,'Superman Returns':4.0,'You,Me and Dupree':1.0}}
from math import sqrt
def sim_distance(prefs,person1,person2):
si={}
for item in prefs[person1]:
if item in prefs[person2]:
si[item]=1
if len(si)==0:
return 0
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))
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])
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:
totals.setdefault(item,0)
totals[item]+=prefs[other][item]*sim
simSums.setdefault(item,0)
simSums[item]+=sim
rankings=[(total/simSums[item],item) for item,total in totals.items()]
rankings.sort()
rankings.reverse()
return rankings