我目前正在研究用户项协作过滤模型。
我有一组用户和他们购物的地方，并试图使用R建立推荐模型。
该项目有两个目标： a）向所有客户推荐新店 b）提供统计数据以显示模型的准确性。
我有2年的数据。
为回答b），我将我的数据子集给了在前1.5年和之后6个月都购买过的客户。 我在前1.5年建立了交易数据模型，然后将模型预测与实际的6个月数据进行了比较。
通过执行上述操作，我确定我将使用UBCF且nn = 500，并且我能够达到约80％的精度。
但是，我现在不确定如何预测整个用户群。 我当时正在考虑将ENTIRE数据集应用于我刚刚创建的模型，但存在偏差/将不准确，因为并非所有商店都以我创建的这个小模型来表示。
我阅读了一些文章和教程，其中人们做了不同的事情。 我看到了一个他们在其中输入整个数据集并应用[which]子集的地方，以便它以80％的比例创建模型并使用剩余的20％的比例进行测试。
我的问题是，如果我要使用此过程，当模型仅给出20％的用户预测时，我将如何为所有用户获得建议？
最好在整个数据集上创建模型吗？
#If in 1.5 years, then 1. If in following 6 months, then 0.
FV$Flag1<-ifelse(FV$Date<="2018-10-01",1,0)
FV$Flag2<-ifelse(FV$Date>"2018-10-01",1,0)
#Create SCV
#FV
FV_SCV<-select(FV, Customer, Flag1, Flag2) %>%
group_by(Customer) %>%
summarise_all(funs(sum)) #Sum all variables.
#Determine which customers to use based on if they have purchased both in the first and second years
FV_SCV$Use<-ifelse(FV_SCV$Flag1>0 & FV_SCV$Flag2>0, 1,0)
#Training. Where customers have purchased both in the first & second year, but we only run the model on the first.
FV_Train<-FV_SCV %>%
filter(Use==1 )
仅将在第一年购买的客户和在过去两年购买的客户中的子集划分为仅在第一年购买的客户
#FV_SCV$flag_sum<- FV_SCV$Flag1+FV_SCV$Flag2
#Join on the USE flag
FV_Train_Transactions<- FV %>% #Join on the page info
left_join(select(FV_Train, Customer, Use), by=c("Customer"="Customer"))
#Replace NA with 0
FV_Train_Transactions[is.na(FV_Train_Transactions)] <- 0
##Subset to only the users' transactions to be used in training
FV_Train_Transactions<-FV_Train_Transactions %>%
filter(Use==1)
##Create date flag for train and test to use to create the model on the train and comparing the results with the output of the test df
FV_Train_Transactions_Compare<-FV_Train_Transactions %>%
filter(Flag2>0)
##Create SCV for TRAIN
FV_TRAIN_SCV<-FV_Train_Transactions %>%
filter(Flag1>0) %>%
group_by(Customer, Brand)%>%
select(Customer, Brand)
FV_TRAIN_SCV$Flag<-1
#Not sure why this hasn't selected unique rows, so remove duplicates.
FV_TRAIN_SCV<-distinct(FV_TRAIN_SCV)
##Create scv for TEST
FV_TEST_SCV<-FV_Train_Transactions_Compare %>%
filter(Flag2>0) %>%
select(Customer, Brand) %>%
group_by(Customer, Brand)
FV_TEST_SCV$Flag<-1
#Not sure why this hasn't selected unique rows, so remove duplicates.
FV_TEST_SCV<-distinct(FV_TEST_SCV)
转置为列
install.packages("reshape")
install.packages("reshape2")
install.packages("tidytext")
library(reshape)
library(reshape2)
library(tidytext)
#Melt data for transposition
#Train
fv_train_md<-melt(FV_TRAIN_SCV, id=(c("Customer", "Brand")))
FV_TRAIN_SCV2<-dcast(fv_train_md, Customer~Brand, value="Flag", fun.aggregate = mean)
#Test
fv_test_md<-melt(FV_TEST_SCV, id=(c("Customer" , "Brand")))
#Do the same for the overall transactions table
#Make FV_SCV a binary rating matrix
fv_overall<- FV[,c(1,3)] #The table name is case sensitive. Select only the customer and brand columns
fv_overall<- distinct(fv_overall) #Remove dups
fv_overall$Flag<-1
fv_overall_md<-melt(fv_overall, id=(c("Customer", "Brand")))
fv_overall_2<- dcast(fv_overall_md, Customer~Brand, value="Flag", fun.aggregate = mean)
#fv_test_123<-dcast(FV_TEST_SCV, Customer~Brand, value.var="Brand")
#colnames(fv_test_123)
#fv_test_12345<-which(fv_test_123==1, arr.ind=TRUE)
#fv_test_123<-colnames(fv_test_123)[fv_test_12345]
#fv_test_123
#fv_test_123_df<-as.data.frame((fv_test_123))
FV_TRAIN_SCV2<-dcast(fv_train_md, Customer~Brand, value="Value", fun.aggregate = mean)
FV_TEST_SCV2<-dcast(fv_test_md, Customer~Brand, value="Value", fun.aggregate = mean)
#Replace NaN with 0
is.nan.data.frame <- function(x)
do.call(cbind, lapply(x, is.nan))
FV_TRAIN_SCV2[is.nan(FV_TRAIN_SCV2)] <- 0
FV_TEST_SCV2[is.nan(FV_TEST_SCV2)] <- 0
fv_overall_2[is.nan(fv_overall_2)] <- 0
install.packages("recommenderlab")
library(recommenderlab)
row.names(FV_TRAIN_SCV2)<-FV_TRAIN_SCV2$Customer
FV_TRAIN_SCV2$Hawkers<-0
FV_TRAIN_SCV2$Pollini<-0
FV_TRAIN_SCV2$"Twin Set"<-0
FV_TRAIN_SCV2_matrix<-as.matrix(FV_TRAIN_SCV2[,2:ncol(FV_TRAIN_SCV2)])
FV_TRAIN_SCV2_binarymatrix<-as(FV_TRAIN_SCV2_matrix,"binaryRatingMatrix")
similarity_FV_train_trans_items<-similarity(FV_TRAIN_SCV2_binarymatrix, method="jaccard", which="items")
train_col<- data.frame(colnames(FV_TRAIN_SCV2))
#------------------------------------------------------------------------------------
row.names(fv_overall_2)<-fv_overall_2$Customer
#Convert NaN to 0
fv_overall_2[is.nan(fv_overall_2)]<-0
#fv_overall_matrix<- as.matrix(fv_overall_2[,2:(ncol(fv_overall_2)-3)])#Convert to matrix
fv_overall_matrix<- as.matrix(fv_overall_2[,2:ncol(fv_overall_2)])#Convert to matrix
#fv_overall_matrix<- as.matrix(fv_overall_matrix[,1:(ncol(fv_overall_2)-3)])
fv_matrix_binary<- as(fv_overall_matrix, "binaryRatingMatrix") #Make a binary ratings matrix
FV_overall_similarity<-similarity(fv_matrix_binary, method="jaccard", which="items")
overall_col<- data.frame(colnames(fv_overall_2))
#---------------------------------------------------------------------------------------------------------
#Now, define multiple recommender algorithms to compare them all.
algorithms <- list(`user-based CF 50` = list(name = "UBCF",param = list(method = "Jaccard", nn = 50)),
`user-based CF 100` = list(name = "UBCF",param = list(method = "Jaccard", nn = 100)),
`user-based CF 200` = list(name = "UBCF",param = list(method = "Jaccard", nn = 200)),
`user-based CF 500` = list(name = "UBCF",param = list(method = "Jaccard", nn = 500)),
#
`item-based CF 3` = list(name = "IBCF",param = list(method = "Jaccard", k = 3)),
`item-based CF 5` = list(name = "IBCF",param = list(method = "Jaccard", k = 5)),
`item-based CF 10` = list(name = "IBCF",param = list(method = "Jaccard", k = 10)),
`item-based CF 50` = list(name = "IBCF",param = list(method = "Jaccard", k = 50))
)
scheme <- evaluationScheme(FV_TRAIN_SCV2_binarymatrix, method = "cross", k = 4,given = 1)
scheme <- evaluationScheme(fv_matrix_binary, method = "cross", k = 4,given = 1)
results <- evaluate(scheme, algorithms, n = c(1,2,3,4,5,6,7,8))
results <- evaluate(scheme, algorithms, n = c(1,2,3,4,5,6,7,8,50,100,200,500))#Evaluating with n=c(1,2,3.....) being the number of recommendations
#names(results) #Check that all results have run.
#results
#Plot results to help determine which of the above models is best for further analysis
#plot(results, annotate = c(1, 3), legend = "right") #ROC Curve
#plot(results, "prec/rec", annotate = 3) #Precision/Recall Plot
这些图的第一个（x轴上有FPR）是ROC曲线。性能最好的模型是具有最大面积的曲线，因此，在这些测试参数中，性能最好的模型是nn = 500的UBCF。或者，使用nn = 50。
基于精度/召回图，应将nn设置为500。
recc_model <- Recommender(data = FV_TRAIN_SCV2_binarymatrix, method = "UBCF",
parameter = list(method = "Jaccard",
nn=500))
model_details <- getModel(recc_model)
model_details
#Running on ENTIRE DATA
recc_model <- Recommender(data = fv_matrix_binary, method = "UBCF",
parameter = list(method = "Jaccard",
nn=500))
model_details <- getModel(recc_model)
model_details
install.packages("plyr")
library(plyr)
#Get the scores
recc_predicted <- predict(object = recc_model, newdata = FV_TRAIN_SCV2_binarymatrix, n = 198, type="ratings")
ibcf_scores<-as(recc_predicted, "list")
ibcf_list_scores<-ldply(ibcf_scores, rbind)
ibcf_list_scores[is.na(ibcf_list_scores)]<-0
#Get the list of brands
recc_predicted_list2<-predict(object=recc_model,newdata= FV_TRAIN_SCV2_binarymatrix,type="topNList", n=198)
recc_predicted_list2_a<- as(recc_predicted_list2, "list")
recc_predicted_list2_b<-ldply(recc_predicted_list2_a, rbind)
#recc_predicted_list2_b[is.na(recc_predicted_list2_b)]<-0
#------------------------------------------------------------
#On the overall model:
#Get the scores
recc_predicted <- predict(object = recc_model, newdata = fv_matrix_binary, n = 80, type="ratings")
ibcf_scores<-as(recc_predicted, "list")
ibcf_list_scores<-ldply(ibcf_scores, rbind)
ibcf_list_scores[is.na(ibcf_list_scores)]<-0
#Get the list of brands
recc_predicted_list2<-predict(object=recc_model,newdata= fv_matrix_binary,type="topNList", n=80)
recc_predicted_list2_a<- as(recc_predicted_list2, "list")
recc_predicted_list2_b<-ldply(recc_predicted_list2_a, rbind)
#recc_predicted_list2_b[is.na(recc_predicted_list2_b)]<-0
install.packages("data.table")
library(data.table)
df.m1<- melt(ibcf_list_scores, id.vars=c(".id"),
value.name="Rating")
df.m1.unique<- data.frame(df.m1)
df.m1.unique$variable<-NULL
df.m1.unique$.id<-NULL
#df.m1.unique<-distinct(df.m1.unique)
#df.m1.unique<- df.m1.unique[order(df.m1.unique$Rating),] #This comma means it is only ordering based on this one var.
#Using ave
df.m1.unique$count<- ave(df.m1.unique$Rating, df.m1.unique[,c("Rating")], FUN=length)
rownames(df.m1.unique) <- c() #Remove rownames
df.m1.unique<-distinct(df.m1.unique)
df.m1.unique<- df.m1.unique[order(-df.m1.unique$Rating),]#Sort by ascending rating
#Plot this
df.m1.unique.plot<- data.frame(df.m1.unique[2:(nrow(df.m1.unique)-1),])
#plot(x=df.m1.unique.plot$Rating, y=df.m1.unique.plot$count)
#Get the cumulative distribution
df.m1.unique.plot2<- df.m1.unique.plot %>%
mutate(Percentage=cumsum(100*(count/sum(count))),
cumsum=cumsum(count))
#a) Remove values that are less than specific rating
#Using logical indexing with replacement
ibcf_list_scores_removal<- ibcf_list_scores
#Replace low values with 0
ibcf_list_scores_removal[,2:ncol(ibcf_list_scores_removal)][ibcf_list_scores_removal[,2:ncol(ibcf_list_scores_removal)] < 0.0217] <- 0
#To flag whether customer is recommended the brand, replace all values >0 with 1. Keep 0 as is.
ibcf_list_scores_removal_b<- ibcf_list_scores_removal #Call a new df
ibcf_list_scores_removal_b[,2:ncol(ibcf_list_scores_removal_b)][ibcf_list_scores_removal_b[,2:ncol(ibcf_list_scores_removal_b)] > 0] <- 1#Create the flag
因此，基本上我想知道如何在ENTIRE数据集上创建模型？ 以及如何提取所有评分？
谢谢