基于用户的协作性过滤推荐系统（为用户推荐影片）

#!/usr/bin/env python
#coding=GBK
import chardet
from math import sqrt

#电影打分， ltl对a电影打了4.3分.....
critics = {'ltl':{'a':4.3, 'b': 3.5, 'c': 4.7}
,'oyl':{'a': 4.3, 'b': 3.5, 'd': 3.0, 'e':4.2}
,'ygq':{'b': 3.2, 'c': 2.7, 'd': 4.1, 'f':2.1}
,'mmm':{'a': 2.3, 'd': 4.3, 'e': 1.9}
}
#求person1， 和perso2之间的相似度
def sim_distance(prefs, person1, person2):
dis = 0
for item in prefs[person1]:
if item in prefs[person2]:
dis += (pow(prefs[person1][item] - prefs[person2][item], 2))

return 1 / (1 + sqrt(dis))

#跟我相似度最大的前n个人
def topMatchs(prefs, person, n = 5, similarity = sim_distance):
scores = [(similarity(prefs, person, other), other)for other in prefs if other != person]
scores.sort()
scores.reverse()
return scores[0:n]
#对没有看过的电影打分
def getRecommendations(prefs, person, similarity = sim_distance):
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]:
totals.setdefault(item, 0)
totals[item] += (sim * prefs[other][item])

simSums.setdefault(item, 0)
simSums[item] += sim
ranks = [(total / simSums[item], item) for item, total in totals.items()]
ranks.sort()
ranks.reverse()
return ranks

ranks = getRecommendations(critics, 'ltl')
for item, item2 in ranks:
en = chardet.detect("item2")['encoding']
print item2, ":", item