php 推荐算法的实现,推荐算法的Python实现（样例代码）

#!/usr/bin/env python

import random

”’

items => {’12’:’PHP’,’1203′:’Storm’,’123′:’Ubuntu’}

items_pool => [12,32,121,324,532,123,53,1203,429,2932]

user_items => {‘1010’:[12,1203,123,429]}

”’

def RandomSelectNagativeSample(items):

ret = dict()

for i in items.keys():

ret[i] = 1

n = 0

for i in range(0,len(items)*3):

item = items_pool[random.randint(0,len(items_pool)-1)]

if item in ret:

continue

ret[item] = 0

n += 1

if n > len(items):

break

return ret

def InitModel(user_items,F):

P = dict()

Q = dict()

for u in user_items.keys():

if u not in P:

P[u] = {}

for f in range(0,F):

P[u][f] = 1

items = user_items.values()

itemLen = len(items[0])

i = 0

while i< itemLen:

ii = items[0][i]

if ii not in Q:

Q[ii] = {}

for f in range(0,F):

Q[ii][f] = 1

i += 1

return [P,Q]

def LatentFactorModel(user_items,F,N,alpha,lambda1):

[P,Q] = InitModel(user_items,F)

for setup in range(0,N):

for user,items in user_items.items():

samples = RandomSelectNagativeSample(items)

for item,rui in samples.items():

eui = rui – Predict(user,item)

for f in range(0,F):

P[user][f] += alpha * (eui * Q[item][f] – lambda1 * P[user][f])

Q[item][f] += alpha * (eui * P[user][f] – lambda1 * Q[item][f])

alpha *= 0.9

return [P,Q]

def Recommend(user,P,Q):

rank = dict()

for f,puf in P[user].items():

for i,pfi in Q[f].items():

if i not in rank:

rank[i] += puf * qfi

return rank

def PersonalRank(G,alpha,root,maxsetup):

rank = dict()

#rank = {x:0 for x in G.keys()}

rank = rank.fromkeys(G.keys(),0)

rank[root] = 1

for k in range(maxsetup):

tmp = dict()

#tmp = {x:0 for x in G.keys()}

tmp = tmp.fromkeys(G.keys(),0)

for i,ri in G.items():

for j,wij in ri.items():

if j not in tmp:

tmp[j] = 0

tmp[j] += alpha * rank[i]/(1.0*len(ri))

if j == root:

tmp[j] += 1 – alpha

rank = tmp

print ‘iter:’ + str(k) + “t”,

for key,value in rank.items():

print “%s:%.3f,t” % (key,value),

print

return rank

if __name__ == ‘__main__’:

G = {‘A’:{‘a’:1,’c’:1},

‘B’:{‘a’:1,’b’:1,’c’:1,’d’:1},

‘C’:{‘c’:1,’d’:1},

‘a’:{‘A’:1,’B’:1},

‘b’:{‘B’:1},

‘c’:{‘A’:1,’B’:1,’C’:1},

‘d’:{‘B’:1,’C’:1}}

PersonalRank(G,0.85,’A’,20)

”’

#items_pool = {’12’:’PHP’,’32’:’Nginx’,’121′:’Apache’,’324′:’Erlang’,’532′:’Linux’,’123′:’Ubuntu’,’53’:’Java’,’1203′:’Storm’,’429′:’Kafka’,’2932′:’Flume’}

items_pool = [12,32,121,324,532,123,53,1203,429,2932]

items = {’12’:’PHP’,’1203′:’Storm’,’123′:’Ubuntu’}

user_items = {‘1010’:[12,1203,123,429]}

#print RandomSelectNagativeSample(items)

print InitModel(user_items,4)

”’