简单的python协同过滤程序实例代码

#coding utf-8import osimport sysimport ref1=open("/home/alber/data_base/bigdata/movielens_train_result.txt",'r')  #读取train文件，已经处理成每一行代表一位用户的item，项之间用空格。f2=open("/home/alber/data_base/bigdata/movielens_train_result3.txt",'a')txt=f1.readlines()contxt=[]f1.close()userdic={}for line in txt:  line_clean=" ".join(line.split())  position=line_clean.index(",")  ID=line_clean[0:position]  item=line_clean[position+1:]  userdic.setdefault(ID,item)  if len(item)>=5:           #对观影量少于5的用户不计入相似性计算的范围    contxt.append(item)for key in userdic.keys():        #计算每位用户的4个最相似用户  ID_num=key  value=userdic[key]   user_item=value.split(' ')   Sim_user=[]   for lines in contxt:     lines_clean=lines.split(' ')     intersection=list(set(lines_clean).intersection(set(user_item)))     lenth_intersection=len(intersection)     difference=list(set(lines_clean).difference(set(user_item)))     lenth_difference=len(difference)     if lenth_difference!=0:                            Similarity=float(lenth_intersection)/lenth_difference          #交集除以差集作为相似性的判断条件       Sim_user.append(Similarity)     else:       Sim_user.append("0")   Sim_user_copy=Sim_user[:]   Sim_user_copy.sort()   Sim_best=Sim_user_copy[-4:]   position1=Sim_user.index(Sim_best[3])   position2=Sim_user.index(Sim_best[2])   position3=Sim_user.index(Sim_best[1])   position4=Sim_user.index(Sim_best[0])   if position1!=0 and position2!=0 and position3!=0 and position4!=0:     recommender=userdic[str(position1)]+" "+userdic[str(position2)]+" "+userdic[str(position3)]+" "+userdic[str(position4)] #将4位用户的看过的电影作为推荐   else:    recommender="none"     reco_list=recommender.split(' ')  recomm=[]  for good in reco_list:    if good not in user_item:      recomm.append(good)    else:      pass  f2.write((" ".join(recomm)+"/n"))f2.close()