如何向所有用户推荐Spark ALS的十大产品？

Question

我们如何才能在PySpark中获得十大推荐产品。据我所知，有些方法会像推荐产品为单个用户推荐产品，并预测所有预测{用户，商品}对的评分。但是，有没有一种有效的方式可以为所有用户输出每个用户的前10个项目？

Answer 1

我写了这个功能，因此，它被分配则通过用户获得每个产品的评级和评价它们排序，并输出的8顶部推荐产品列表中的分区乘以用户特征和产品特性。

#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) 

Answer 2

PySpark> = 1.6.0提供MatrixFactorizationModel.recommendProductsForUsers：

>> model.recommendProductsForUsers(3).take(2) 
[(1, 
    (Rating(user=1, product=2975, rating=0.003626774788608227), 
    Rating(user=1, product=1322, rating=0.002494393082165332), 
    Rating(user=1, product=8746, rating=0.002176665942528324))), 
(2, 
    (Rating(user=2, product=4060, rating=0.011020947406565042), 
    Rating(user=2, product=2332, rating=0.009479319983658458), 
    Rating(user=2, product=1979, rating=0.004587168057824856)))]