1. [文件]
cosine.py
#-*- coding: utf-8 -*-
'''
Created on 2012-9-3
@author: Jekey
余弦相关性，如果数据稀疏，考虑使用该算法
'''
import codecs
from math import sqrt
users = {"Angelica": {"Blues Traveler": 3.5, "Broken Bells": 2.0, "Norah Jones": 4.5, "Phoenix": 5.0, "Slightly Stoopid": 1.5, "The Strokes": 2.5, "Vampire Weekend": 2.0},
"Bill":{"Blues Traveler": 2.0, "Broken Bells": 3.5, "Deadmau5": 4.0, "Phoenix": 2.0, "Slightly Stoopid": 3.5, "Vampire Weekend": 3.0},
"Chan": {"Blues Traveler": 5.0, "Broken Bells": 1.0, "Deadmau5": 1.0, "Norah Jones": 3.0, "Phoenix": 5, "Slightly Stoopid": 1.0},
"Dan": {"Blues Traveler": 3.0, "Broken Bells": 4.0, "Deadmau5": 4.5, "Phoenix": 3.0, "Slightly Stoopid": 4.5, "The Strokes": 4.0, "Vampire Weekend": 2.0},
"Hailey": {"Broken Bells": 4.0, "Deadmau5": 1.0, "Norah Jones": 4.0, "The Strokes": 4.0, "Vampire Weekend": 1.0},
"Jordyn": {"Broken Bells": 4.5, "Deadmau5": 4.0, "Norah Jones": 5.0, "Phoenix": 5.0, "Slightly Stoopid": 4.5, "The Strokes": 4.0, "Vampire Weekend": 4.0},
"Sam": {"Blues Traveler": 5.0, "Broken Bells": 2.0, "Norah Jones": 3.0, "Phoenix": 5.0, "Slightly Stoopid": 4.0, "The Strokes": 5.0},
"Veronica": {"Blues Traveler": 3.0, "Norah Jones": 5.0, "Phoenix": 4.0, "Slightly Stoopid": 2.5, "The Strokes": 3.0}
}
#cosine 距离
def cosine(rate1,rate2):
sum_xy = 0
sum_x=0
sum_y=0
n=0
for key in rate1:
if key in rate2:
n+=1
x=rate1[key]
y=rate2[key]
sum_xy += x*y
sum_x +=x*x
sum_y +=y*y
#计算距离
if n==0:
return 0
else:
sx=pow(sum_x,1/2)
sy=pow(sum_y,1/2)
if sum_xy<>0:
denominator=sx*sy/sum_xy
else:
denominator=0
return denominator
#返回最近距离用户
def computeNearestNeighbor(username,users):
distances = []
for key in users:
if key<>username:
distance = cosine(users[username],users[key])
distances.append((distance,key))
distances.sort()
return distances
#推荐
def recommend(username,users):
#获得最近用户的name
nearest = computeNearestNeighbor(username,users)[0][1]
recommendations =[]
#得到最近用户的推荐列表
neighborRatings = users[nearest]
for key in neighborRatings:
if not key in users[username]:
recommendations.append((key,neighborRatings[key]))
recommendations.sort(key=lambda rat:rat[1], reverse=True)
return recommendations
if __name__ == '__main__':
print recommend('Hailey', users)
