用R解析mahout用户推荐协同过滤算法

##建立模型

FileDataModel=function(file){
data=read.csv(file,header=F)
names(data)=c('uid','iid','pref')
user=unique(data$uid)
item=unique(sort(data$iid))
uidx=match(data$uid,user)
iidx=match(data$iid,item)
M=matrix(0,length(user),length(item))
i=cbind(uidx,iidx,pref=data$pref)
for(n in 1:nrow(i)){
M[i[n,][1],i[n,][2]]=i[n,][3]

}
dimnames(M)[[2]]=item
M
}

##欧式距离相似度算法
EuclideanDistanceSimilarity=function(M){
row=nrow(M)
s=matrix(0,row,row)
for(z1 in 1:row){
for(z2 in 1:row){
if(z1<z2){
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num=intersect(which(M[z1,]!=0),which(M[z2,]!=0)) #可计算的列

sum=0
for(z3 in num ){
sum<-sum+(M[z1,][z3]-M[z2,][z3])^2
}
s[z2,z1]<-length(num)/(1+sqrt(sum))

if(s[z2,z1]>1) s[z2,z1]<-1 #标准化
if(s[z2,z1]< -1) s[z2,z1]<- -1 #标准化

#print(paste(z1,z2));print(num);print(sum)

}
}
}
ts<-t(s)
w<-which(upper.tri(ts))
s[w]<-ts[w]
s
}

##最紧邻算法

NearestNUserNeighborhood<-function(S,n){
row<-nrow(S)
neighbor<-matrix(0, row, n)
for(z1 in 1:row){
for(z2 in 1:n){
m<-which.max(S[,z1])
#       print(paste(z1,z2,m,'\n'))
neighbor[z1,][z2]<-m
S[,z1][m]=0
}
}
neighbor
}

###4). 推荐算法

UserBasedRecommender<-function(uid,n,M,S,N){
row<-ncol(N)
col<-ncol(M)
r<-matrix(0, row, col)
N1<-N[uid,]
for(z1 in 1:length(N1)){
num<-intersect(which(M[uid,]==0),which(M[N1[z1],]!=0)) #可计算的列
#     print(num)

for(z2 in num){
#       print(paste("for:",z1,N1[z1],z2,M[N1[z1],z2],S[uid,N1[z1]]))
r[z1,z2]=M[N1[z1],z2]*S[uid,N1[z1]]
}
}

sum<-colSums(r)
s2<-matrix(0, 2, col)
for(z1 in 1:length(N1)){
num<-intersect(which(colSums(r)!=0),which(M[N1[z1],]!=0))
for(z2 in num){
s2[1,][z2]<-s2[1,][z2]+S[uid,N1[z1]]
s2[2,][z2]<-s2[2,][z2]+1
}
}

s2[,which(s2[2,]==1)]=10000
s2<-s2[-2,]

r2<-matrix(0, n, 2)
rr<-sum/s2
item <-dimnames(M)[[2]]
for(z1 in 1:n){
w<-which.max(rr)
if(rr[w]>0.5){
r2[z1,1]<-item[which.max(rr)]
r2[z1,2]<-as.double(rr[w])
rr[w]=0
}
}
r2
}

5). 运行程序

FILE<-"testCF.csv"
NEIGHBORHOOD_NUM<-2
RECOMMENDER_NUM<-3

M<-FileDataModel(FILE)
S<-EuclideanDistanceSimilarity(M)
N<-NearestNUserNeighborhood(S,NEIGHBORHOOD_NUM)

R1<-UserBasedRecommender(1,RECOMMENDER_NUM,M,S,N);R1
##      [,1]  [,2]
## [1,] "104" "4.25"
## [2,] "106" "4"
## [3,] "0"   "0"

R2<-UserBasedRecommender(2,RECOMMENDER_NUM,M,S,N);R2
##      [,1]  [,2]
## [1,] "105" "3.95699903407931"
## [2,] "0"   "0"
## [3,] "0"   "0"

R3<-UserBasedRecommender(3,RECOMMENDER_NUM,M,S,N);R3
##      [,1]  [,2]
## [1,] "103" "3.18540697329411"
## [2,] "102" "2.80243217111765"
## [3,] "0"   "0"

R4<-UserBasedRecommender(4,RECOMMENDER_NUM,M,S,N);R4
##      [,1]  [,2]
## [1,] "102" "3"
## [2,] "0"   "0"
## [3,] "0"   "0"

R5<-UserBasedRecommender(5,RECOMMENDER_NUM,M,S,N);R5
##      [,1] [,2]
## [1,]    0    0
## [2,]    0    0
## [3,]    0    0