常見推薦演算法Code總結
阿新 • • 發佈：2019-01-30
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#!/usr/bin/env python
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import random
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'''
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items => {'12':'PHP','1203':'Storm','123':'Ubuntu'}
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items_pool => [12,32,121,324,532,123,53,1203,429,2932]
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user_items => {'1010':[12,1203,123,429]}
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'''
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def RandomSelectNagativeSample(items):
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ret = dict()
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for i in items.keys():
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ret[i] = 1
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n = 0
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for i in range(0,len(items)*3):
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item = items_pool[random.randint(0,len(items_pool)-1)]
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if item in ret:
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continue
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ret[item] = 0
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n += 1
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if n > len(items):
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break
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return ret
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def InitModel(user_items,F):
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P = dict()
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Q = dict()
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for u in user_items.keys():
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if u not in P:
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P[u] = {}
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for f in range(0,F):
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P[u][f] = 1
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items = user_items.values()
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itemLen = len(items[0])
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i = 0
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while i< itemLen:
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ii = items[0][i]
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if ii not in Q:
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Q[ii] = {}
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for f in range(0,F):
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Q[ii][f] = 1
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i += 1
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return [P,Q]
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def LatentFactorModel(user_items,F,N,alpha,lambda1):
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[P,Q] = InitModel(user_items,F)
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for setup in range(0,N):
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for user,items in user_items.items():
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samples = RandomSelectNagativeSample(items)
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for item,rui in samples.items():
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eui = rui - Predict(user,item)
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for f in range(0,F):
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P[user][f] += alpha * (eui * Q[item][f] - lambda1 * P[user][f])
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Q[item][f] += alpha * (eui * P[user][f] - lambda1 * Q[item][f])
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alpha *= 0.9
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return [P,Q]
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def Recommend(user,P,Q):
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rank = dict()
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for f,puf in P[user].items():
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for i,pfi in Q[f].items():
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if i not in rank:
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rank[i] += puf * qfi
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return rank
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def PersonalRank(G,alpha,root,maxsetup):
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rank = dict()
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#rank = {x:0 for x in G.keys()}
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rank = rank.fromkeys(G.keys(),0)
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rank[root] = 1
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for k in range(maxsetup):
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tmp = dict()
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#tmp = {x:0 for x in G.keys()}
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tmp = tmp.fromkeys(G.keys(),0)
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for i,ri in G.items():
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for j,wij in ri.items():
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if j not in tmp:
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tmp[j] = 0
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tmp[j] += alpha * rank[i]/(1.0*len(ri))
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if j == root:
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tmp[j] += 1 - alpha
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rank = tmp
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print 'iter:' + str(k) + "\t",
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for key,value in rank.items():
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print "%s:%.3f,\t" % (key,value),
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print
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return rank
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if __name__ == '__main__':
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G = {'A':{'a':1,'c':1},
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'B':{'a':1,'b':1,'c':1,'d':1},
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'C':{'c':1,'d':1},
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'a':{'A':1,'B':1},
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'b':{'B':1},
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'c':{'A':1,'B':1,'C':1},
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'd':{'B':1,'C':1}}
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PersonalRank(G,0.85,'A',20)
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'''
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#items_pool = {'12':'PHP','32':'Nginx','121':'Apache','324':'Erlang','532':'Linux','123':'Ubuntu','53':'Java','1203':'Storm','429':'Kafka','2932':'Flume'}
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items_pool = [12,32,121,324,532,123,53,1203,429,2932]
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items = {'12':'PHP','1203':'Storm','123':'Ubuntu'}
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user_items = {'1010':[12,1203,123,429]}
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#print RandomSelectNagativeSample(items)
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print InitModel(user_items,4)
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