个性化服务推荐

1、基于二部图的个性化系统推荐：#include<iostream>#include<fstream>#include<vector>#include<string>#include<vector>#include<algorithm>#include<iomanip>usingnamespacestd;/从TXT中读入数据到矩阵(二维数组)template<typenameT>vector<vector<T>>txtRead(stringrow,intcol)ifstreami

2、nput();if(!input.is_open()cerr<<"notexisting,checkthepath:n"<<<<endl;exit(1);vector<vector<T>>data(row,vector<T>(col,0);for(inti=0;i<row;+i)for(intj=0;j<col;+j)/因为这里针对的情况是用户只给出对items的喜欢与不喜欢的情况，而movielens/是一个1-5的评分数据，所以把分数达到3的看作是喜欢，标记为1，小于3的视为/不喜欢，置

3、为0input>>dataij;if(dataij>=3)dataij=1;elsedataij=0;returndata;/把矩阵中的数据写入TXT文件template<typenameT>voidtxtWrite(vector<vector<T>>Matrix,stringdest)ofstreamoutput(dest);vector<vector<T>>:size_typerow=Matrix.size();vector<T>:size_typecol=Matrix0.size();for(vec

4、tor<vector<T>>:size_typei=0;i<row;+i)for(vector<T>:size_typej=0;j<col;+j)output<<setprecision(3)<<Matrixij<<"t"output<<endl;/求两个向量的内积doubleInnerProduct(std:vector<double>A,std:vector<double>B)doubleres=0;for(std:vector<double&g

5、t;:size_typei=0;i<A.size();+i)res+=Ai*Bi;returnres;/矩阵转置操作template<typenameT>/vector<vector<T>>Transpose(vector<vector<T>>Matrix)unsignedrow=Matrix.size();unsignedcol=Matrix0.size();vector<vector<T>>Trans(col,vector<T>(row,0);for(unsignedi=0;i<co

6、l;+i)for(unsignedj=0;j<row;+j)Transij=Matrixji;returnTrans;/求一个向量中所有元素的和template<typenameT>TSumVector(vector<T>vec)Tres=0;for(vector<T>:size_typei=0;i<vec.size();+i)res+=veci;returnres;/对一个向量中的元素进行降序排列，返回重排后的元素在原来/向量中的索引boolIsBigger(doublea,doubleb)returna>=b;vector<uns

7、igned>DescendVector(vector<double>vec)vector<double>tmpVec=vec;sort(tmpVec.begin(),tmpVec.end(),IsBigger);vector<unsigned>idx;for(vector<double>:size_typei=0;i<tmpVec.size();+i)for(vector<double>:size_typej=0;j<vec.size();+j)if(tmpVeci=vecj)idx.push_back(j);ret

8、urnidx;基于概率传播(ProbS)的二部图的推荐函数，data是训练数据vector<vector<double>>ProbS(vector<vector<double>>data)autorow=data.size();autocol=data0.size();vector<vector<double>>transData=Transpose(data);/第一步利用概率传播机制计算权重矩阵vector<vector<double>>weights(col,vector<double&

9、gt;(col,0);for(vector<double>:size_typei=0;i<col;+i)for(vector<double>:size_typej=0;j<col;+j)doubledegree=SumVector<double>(transDataj);doublesum=0;for(vector<double>:size_typek=0;k<row;+k)sum+=transDataik*transDatajk/SumVector<double>(datak);if(degree)weightsi

10、j=sum/degree;/第二步利用权重矩阵和训练数据集针对每个用户对每一个item评分vector<vector<double>>scores(row,vector<double>(col,0);for(vector<double>:size_typei=0;i<row;+i)for(vector<double>:size_typej=0;j<col;+j)/等于0的地方代表useri还木有评价过itemj,需要预测if(0=dataij)scoresij=InnerProduct(weightsj,datai);re

11、turnscores;/计算推荐结果的命中率:推荐的items中用户确实喜欢的items数量/推荐的items数量/用户确实喜欢的items是由测试集给出,length表示推荐列表最长为多少，这里将测出/推荐列表长度由1已知增加到length过程中，推荐命中率的变化vector<vector<double>>ComputeHitRate(vector<vector<double>>scores,vector<vector<double>>test,unsignedlength)autousersNum=test.size(

12、);autoitemsNum=test0.size();vector<vector<unsigned>>sortedIndex;/因为只是对测试集中的用户和items进行测试，于是选取与测试集大小一样的预测数据vector<vector<double>>selectedScores(usersNum,vector<double>(itemsNum,0);vector<double>line;for(unsignedi=0;i<usersNum;+i)for(unsignedj=0;j<itemsNum;+j)l

13、ine.push_back(scoresij);sortedIndex.push_back(DescendVector(line);line.clear();/hitRate的第一列存储推荐列表的长度，第二列存储对应的命中率vector<vector<double>>hitRate(length);for(unsignedk=1;k<=length;+k)hitRatek-1.push_back(k);doubleCounter=0;for(unsignedi=0;i<usersNum;+i)for(unsignedj=0;j<k;+j)unsigne

14、ditemIndex=sortedIndexij;if(testiitemIndex)+Counter;hitRatek-1.push_back(Counter/(k*usersNum);returnhitRate;intmain()string("data.txt");/string("E:Matlabcoderecommendationsystemdatamovielenstest.txt");introw=10;intcol=10;cout<<"数据读取中."<<endl;vector<vector

15、<double>>train=txtRead<double>(,row,col);/vector<vector<double>>test=txtRead<double>(,462,1591);cout<<"利用二部图网络进行评分预测."<<endl;vector<vector<double>>predictScores=ProbS(train);txtWrite(predictScores,"predictScores.txt");/*cou

16、t<<"计算命中率."<<endl;vector<vector<double>>hitRate=ComputeHitRate(predictScores,test,1591);txtWrite(hitRate,"hitRate.txt");cout<<"命中率结果保存完毕！"<<endl;*/return0;下面是用户协同过滤核心代码：#include<iostream>#include<fstream>#include<stdio.

17、h>#include<map>#include<set>#include<vector>#include<cstdlib>#include<cmath>#include<cstring>#include<algorithm>usingnamespacestd;constintMAX=1000;classUserCFprivate:intk,m;/k:themostkpersonsinterestedinthebrandi;m:choosetheformermbrandswhichuseruaremosti

18、nterestedinmap<int,int>userid_id;map<int,int>id_userid;map<int,set<int>>user_brands;map<int,set<int>>id_brands;map<int,set<int>>brand_ids;set<int>brand_all;map<int,set<int>>user_brand_rec;ifstreamfin;ofstreamfout;doublesim_matMAXMAX;t

19、ypedefstructsim_idxdoublesim;intidx;booloperator>(constsim_idx&other)constreturnsim>other.sim;sim_idx;typedefstructbrand_interestdoubleInt;/Int=interestintbrand;booloperator>(constbrand_interest&other)constreturnInt>other.Int;brand_interest;public:UserCF(int_k,int_m):k(_k),m(_m)f

20、in.open("user_brand_m123.txt");fout.open("rec_result_by_m123.txt");if(!fin|!fout)cout<<"cannotopenthefile"<<endl;exit(1);/userid_id,id_userid,user_brands,brand_allintuserid,brandid,i=0;while(fin>>userid>>brandid)if(user_brandsuserid.empty()userid

21、_iduserid=i;id_useridi=userid;i+;user_brandsuserid.insert(brandid);brand_all.insert(brandid);/id_brandsmap<int,set<int>>:iteratorit=user_brands.begin();while(it!=user_brands.end()id_brandsuserid_idit->first=it->second;it+;bzero(sim_mat,0);voidget_sim_mat()get_reverse_table();map<

22、;int,set<int>>:iteratorit=brand_ids.begin();while(it!=brand_ids.end()vector<int>tmp(it->second.begin(),it->second.end();intlen=tmp.size();/foreachbrand,traversealltwopairusers,sim_matincrementfor(inti=0;i<len;i+)for(intj=i+1;j<len;j+)sim_mattmpitmpj+=1;sim_mattmpjtmpi+=1;i

23、t+;intlen=id_userid.size();for(inti=0;i<len;i+)for(intj=0;j<len;j+)sim_matij/=sqrt(user_brandsid_useridi.size()*user_brandsid_useridj.size();sim_matji=sim_matij;cout<<endl;/brand_ids-brand:userid1,userid2,userid3.voidget_reverse_table()map<int,set<int>>:iteratorit=id_brands.b

24、egin();while(it!=id_brands.end()set<int>tmp=it->second;set<int>:iteratorit2=tmp.begin();while(it2!=tmp.end()brand_ids*it2.insert(it->first);it2+;it+;set<int>get_rec_brand_set_by_user(intuserid)/3.1set<int>brand_unused;/*set_difference:finddifferentsetbetweentwoset*funct

25、ion:getbrandsetthatuseridhasneverboughtbefore*/set_difference(brand_all.begin(),brand_all.end(),user_brandsuserid.begin(),user_brandsuserid.end(),inserter(brand_unused,brand_unused.begin();sim_idxsimidx;vector<sim_idx>vec_sim_idx;intlen=userid_id.size();intid=userid_iduserid;for(inti=0;i<le

26、n;i+)simidx.sim=sim_matidi;simidx.idx=i;vec_sim_idx.push_back(simidx);sort(vec_sim_idx.begin(),vec_sim_idx.end(),greater<sim_idx>();/orderbydesc/3.2.1set<int>rec_ids;vector<sim_idx>:iteratorit=vec_sim_idx.begin();for(inti=0;i<k;i+)rec_ids.insert(*it).idx);it+;set<int>rec_b

27、rand;set<int>:iteratoritt=brand_unused.begin();vector<brand_interest>vec_bi;/userid'sinterestleveltowardbrandbrand_interestbi;while(itt!=brand_unused.end()/3.2.2vector<int>newset;/orset<int>newsetset<int>ids=brand_ids*itt;set_intersection(rec_ids.begin(),rec_ids.end

28、(),ids.begin(),ids.end(),inserter(newset,newset.begin();if(newset.empty()itt+;continue;doubleinterest=0.0;intlen=newset.size();for(inti=0;i<len;i+)interest+=sim_matuserid_iduseridnewseti;/3.2.3bi.brand=*itt;bi.Int=interest;vec_bi.push_back(bi);itt+;/while/sort(vec_bi.begin(),vec_bi.end(),greater&

29、lt;brand_interest>();/感谢chenyadong的修改建议，这行代码要进行添加/3.3for(inti=0;i<m&&i<vec_bi.size();i+)rec_brand.insert(vec_bii.brand);returnrec_brand;voidrecommend()map<int,int>:iteratorit=userid_id.begin();while(it!=userid_id.end()user_brand_recit->first=get_rec_brand_set_by_user(it->

30、;first);it+;/recommendvoidprint()/writerecommendationresult<user,brand>tofilemap<int,set<int>>:iteratorit=user_brand_rec.begin();while(it!=user_brand_rec.end()set<int>tmp=it->second;set<int>:iteratorit2=tmp.begin();while(it2!=tmp.end()fout<<it->first<<

31、""<<*it2<<endl;it2+;it+;/printUserCF()userid_id.clear();user_brands.clear();id_brands.clear();brand_ids.clear();brand_all.clear();user_brand_rec.clear();fin.close();fout.close();intmain(intargc,char*argv)if(argc!=3)cout<<"Usage:./a.outkm"<<endl;exit(1);int

32、k=atoi(argv1);intm=atoi(argv2);UserCFucf(k,m);ucf.get_sim_mat();ucf.recommend();#if1ucf.print();#endifreturn0;下面是计算准确率和召回率代码：#include<iostream>#include<fstream>#include<map>#include<set>#include<algorithm>usingnamespacestd;intmain()ifstreamfin,fin1;fin.open("rec_re

33、sult_by_m123.txt");fin1.open("user_brand_m4.txt");if(!fin|!fin1)cout<<"cannotopenfile"<<endl;exit(1);map<int,set<int>>fore_user_brands;/recommendationresultmap<int,set<int>>real_user_brands;/realresultintuser,brand;while(fin>>user>

34、;>brand)fore_user_brandsuser.insert(brand);while(fin1>>user>>brand)real_user_brandsuser.insert(brand);doubleprecision=0,recall=0;doublefore_total_brand=0;doublereal_total_brand=0;doubleintersection=0;doubleF=0;map<int,set<int>>:iteratorit=fore_user_brands.begin();map<int,set<int>>:iteratoritt=real_user_brands.begin();while(it!=fore_user_brands.end()fore_total_