如何为所有用户推荐Spark ALS的十大产品？

Question

我们怎样才能在PySpark中获得十大推荐产品.我知道有一些方法,例如recommendedProducts为单个用户推荐产品,而且预测全部用于预测{user,item}对的评级.但是有没有一种有效的方法可以为所有用户输出每个用户的前10项？

Answer 1

我编写了这个函数,它通过分区将用户功能和产品功能相乘,然后分配,然后由用户获取每个产品的评级,并通过评级对其进行排序,并输出8个推荐产品的列表.

#Collect product feature matrix
 productFeatures = bestModel.productFeatures().collect() 
 productArray=[]
 productFeaturesArray=[]
 for x in productFeatures:
    productArray.append(x[0])
    productFeaturesArray.append(x[1])  
 matrix=np.matrix(productFeaturesArray)
 productArrayBroadCast=sc.broadcast(productArray)
 productFeaturesArraybroadcast=sc.broadcast(matrix.T)

 def func(iterator):
      userFeaturesArray = []
      userArray = []
      for x in iterator:
          userArray.append(x[0])
          userFeaturesArray.append(x[1])
          userFeatureMatrix = np.matrix(userFeaturesArray)
          userRecommendationArray = userFeatureMatrix*(productFeaturesArraybroadcast.value)
          mappedUserRecommendationArray = []
          #Extract ratings from the matrix
          i=0
          for i in range(0,len(userArray)):
              ratingdict={}
              j=0
              for j in range(0,len(productArrayBroadcast.value)):
                   ratingdict[str(productArrayBroadcast.value[j])]=userRecommendationArray.item((i,j))
                   j=j+1
              #Take the top 8 recommendations for the user
              sort_apps=sorted(ratingdict.keys(), key=lambda x: x[1])[:8]
              sort_apps='|'.join(sort_apps)
              mappedUserRecommendationArray.append((userArray[i],sort_apps))
              i=i+1
      return [x for x in mappedUserRecommendationArray]


recommendations=model.userFeatures().repartition(2000).mapPartitions(func)