# -*- coding:UTF-8 -*-
import sys
def PrepareMData(sc,path):
print("========读取电影信息,from "+path+"/data/u.item")
itemRDD=sc.textFile(path+"/data/u.item")
#itemRDD.cache()
movieTitle = itemRDD.map(lambda line: line.split("|")) \
.map(lambda a: (float(a[0]), a[1])) \
.collectAsMap()
return movieTitle
def loadModel(sc,path):
#from pyspark.mllib.recommendation import ALS
from pyspark.mllib.recommendation import MatrixFactorizationModel
model=None
try:
model=MatrixFactorizationModel.load(sc,path+"/ALSmodel")
model.userFeatures().cache()
model.productFeatures().cache()
print("=== 载入ALSmode，from "+path)
except Exception as e:
print "无法载入ALSmodel"+e.message
return model
def Recommend(model,movieTitle):
if sys.argv[1]=="--U":
RecommendMovies(model,movieTitle,int(sys.argv[2]))
elif sys.argv[1]=="--M":
RecommendUsers(model,movieTitle,int(sys.argv[2]))
def RecommendMovies(model,movieTitle,inputUserID):
recMovie=model.recommendProducts(inputUserID,10)
print ("针对用户"+str(inputUserID)+" 推荐电影：")
for rmd in recMovie:
print("针对用户ID:{0},推荐电影ID:{1},电影名:{2},推荐评分:{3}").\
format (rmd[0],rmd[1],movieTitle[rmd[1]],rmd[2])
def RecommendUsers(model,movieTitle,inputMovieID):
recUser=model.recommendUsers(inputMovieID,10)
print ("针对电影id：{0} 电影名：{1},推荐下列用户ID：").\
format(inputMovieID,movieTitle[inputMovieID])
for rmd in recUser:
print("用户ID:{0},推荐评分:{1}").\
format (rmd[0],rmd[2])
if __name__ == "__main__":
from ALS.Com import *
if len(sys.argv) != 3:
print("请输入2个参数")
exit(-1)
sc = CreateSparkContext()
print ("======== 推荐程序，数据准备 ========")
path = SetPath(sc)
movieTitle = PrepareMData(sc,path)
print ("======== 推荐程序，载入模型 ========")
model=loadModel(sc,path)
#model.userFeatures().cache()
#model.productFeatures().cache()
if(model!=None):
print ("====== 进行推荐 ==========")
Recommend(model,movieTitle)
