php用户推荐算法,基于用户的推荐算法余弦相似性实现

1.[文件]

cosine.py#-*- coding: utf-8 -*-

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Created on 2012-9-3

&＃64;author: Jekey

余弦相关性&＃xff0c;如果数据稀疏&＃xff0c;考虑使用该算法

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import codecs

from math import sqrt

users &＃61; {"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 &＃61; 0

sum_x&＃61;0

sum_y&＃61;0

n&＃61;0

for key in rate1:

if key in rate2:

n&＃43;&＃61;1

x&＃61;rate1[key]

y&＃61;rate2[key]

sum_xy &＃43;&＃61; x*y

sum_x &＃43;&＃61;x*x

sum_y &＃43;&＃61;y*y

#计算距离

if n&＃61;&＃61;0:

return 0

else:

sx&＃61;pow(sum_x,1/2)

sy&＃61;pow(sum_y,1/2)

if sum_xy<>0:

denominator&＃61;sx*sy/sum_xy

else:

denominator&＃61;0

return denominator

#返回最近距离用户

def computeNearestNeighbor(username,users):

distances &＃61; []

for key in users:

if key<>username:

distance &＃61; 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 &＃61; computeNearestNeighbor(username,users)[0][1]

recommendations &＃61;[]

#得到最近用户的推荐列表

neighborRatings &＃61; users[nearest]

for key in neighborRatings:

if not key in users[username]:

recommendations.append((key,neighborRatings[key]))

recommendations.sort(key&＃61;lambda rat:rat[1], reverse&＃61;True)

return recommendations

if __name__ &＃61;&＃61; &＃39;__main__&＃39;:

print recommend(&＃39;Hailey&＃39;, users)

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