Python用户推荐系统minkowski算法的实现

#-*- coding: utf-8 -*-

'''

python: www.iplaypy.com

@author: Jekey

欧几里得距离

如果数据很稠密，使用该算法较好

'''

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}

}

#Minkowski 距离

def minkowski(rate1,rate2,r):

distance = 0

commonRating = False

for key in rate1:

if key in rate2:

distance+=pow(abs(rate1[key]-rate2[key]),r)

commonRating=True

if commonRating:

return pow(distance,1/r)

else:

return -1

#返回最近距离用户

def computeNearestNeighbor(username,users):

distances = []

for key in users:

if key<>username:

distance = minkowski(users[username],users[key],2)

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)

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