用户项目的亲和力和建议：
我正在创建一个表，建议“购买此商品的客户也购买了算法”
输入数据集

productId   userId
Prod1        a
Prod1        b
Prod1        c
Prod1        d
prod2        b
prod2        c
prod2        a
prod2        b
prod3        c
prod3        a
prod3        d
prod3        c
prod4        a
prod4        b
prod4        d
prod4        a
prod5        d
prod5        a

需要输出

Product1    Product2    score
Prod1       prod3
Prod1       prod4
Prod1       prod5
prod2       Prod1
prod2       prod3
prod2       prod4
prod2       prod5
prod3       Prod1
prod3       prod2



Using code : 
#Get list of unique items
itemList=list(set(main["productId"].tolist()))

#Get count of users
userCount=len(set(main["productId"].tolist()))

#Create an empty data frame to store item affinity scores for items.
itemAffinity= pd.DataFrame(columns=('item1', 'item2', 'score'))
rowCount=0

#For each item in the list, compare with other items.
for ind1 in range(len(itemList)):

    #Get list of users who bought this item 1.
    item1Users = main[main.productId==itemList[ind1]]["userId"].tolist()
    #print("Item 1 ", item1Users)

    #Get item 2 - items that are not item 1 or those that are not analyzed already.
    for ind2 in range(ind1, len(itemList)):

        if ( ind1 == ind2):
            continue

        #Get list of users who bought item 2
        item2Users=main[main.productId==itemList[ind2]]["userId"].tolist()
        #print("Item 2",item2Users)

        #Find score. Find the common list of users and divide it by the total users.
        commonUsers= len(set(item1Users).intersection(set(item2Users)))
        score=commonUsers / userCount

        #Add a score for item 1, item 2
        itemAffinity.loc[rowCount] = [itemList[ind1],itemList[ind2],score]
        rowCount +=1
        #Add a score for item2, item 1. The same score would apply irrespective of the sequence.
        itemAffinity.loc[rowCount] = [itemList[ind2],itemList[ind1],score]
        rowCount +=1

#Check final result
itemAffinity

该代码在示例数据集上运行良好，但是
该代码花费的时间太长，无法在包含100,000行的数据集中运行。请帮助我优化代码。