|IS 643 CURRENT TOPICS IN DATA ANALYTICS - PROJECT | Data Analytics
Author:MUSA T GANIYU
April 20th, 2017
Objective:
In the project, we are doing to deal with recommender system. A system that recommends products or services to user based on their past experience. We will focus more on Collaborative Filtering and Content-Based Recommender System. Although, we have a Knowledged-Based Recommender System, Hybrid System that combine the two together, but our focus on this project would be based on the former.
You may want to ask, what is COllaborative Filtering?. A Collaborative Filtering is a method of making automatic predictions or filtering about the interests of a user by collecting preferences or taste information from many users.Similarity of items is determined by the similarity of the ratings of those items by the users who have rated both items.
Colaborative filtering make use of algorithms that require the following: * Users’ active participation * User interest representation * Matching people with similar interest together using algorithm.
Content-Based: This systems focus on properties of items. Similarity of items is determined by measuring the similarity in their properties.
Methodology:
- We will build an Item-Item determining relationship between pairs of items
- we will also infer the tastes of current user by examing the matrix and matching that user’s data.
We will be making use of both Spark and Base R environment.
Kindly install the packages below before loading it(If you dont already have them installed).
options(warn = -1)
suppressMessages(library(knitr))
suppressMessages(library(sparklyr))
suppressMessages(library(SparkR))
suppressMessages(library(dplyr))
suppressMessages(library(randomForest))
suppressMessages(library(magrittr))
suppressMessages(library(reshape2))
suppressMessages(library(recommenderlab))
suppressMessages(library(tidyr))
suppressWarnings(library(ggplot2))##
## Attaching package: 'ggplot2'## The following object is masked from 'package:randomForest':
##
## marginsuppressMessages(library(e1071))Euclidean Distance:
This is distance between two points in the plane with coordinates(x,y) and (a,b) which can also be mathematically represented as
\(Euclidean\quad Distaince(x,y)\quad =\quad \sqrt { \sum _{ i=1 }^{ n }{ { |{ x }_{ i } }-\quad { y }_{ i }|^{ 2 } } }\)
Correlation:
This a statistical technique that is used to show and determine how strongly pairs of variables are related.
\({ \rho }_{ x,y }\quad =\frac { cov(X,Y) }{ { \sigma }_{ x }{ \sigma }_{ y } }\)
Distance:
This is majorly used in a binary datasets to determine the distance between two items.
\(distance(ItemA,ItemB)\quad =\quad \frac { ItemA\cap ItemB }{ ItemA\cup ItemB }\)
We are going to connect spark using our local machine.
# LOCAL CONNECTION:
sc <- spark_connect(master = "local", version = "2.1.0")Checking if Spark is connected or not
spark_connection_is_open(sc)## [1] TRUERead the dataset from the local machine and renaming the columns as appropriate.
ratings_df <- read.csv("C:/Users/mayowa/Downloads/ml-latest-small/ml-latest-small/ratings.csv", head=T)
movies_df <- read.csv("C:/Users/mayowa/Downloads/ml-latest-small/ml-latest-small/movies.csv", head=T)
colnames(ratings_df)[1:3] <- c("user","item","rating")
colnames(movies_df)[1:3] <- c("user","title","genres")head(ratings_df)## user item rating timestamp
## 1 1 31 2.5 1260759144
## 2 1 1029 3.0 1260759179
## 3 1 1061 3.0 1260759182
## 4 1 1129 2.0 1260759185
## 5 1 1172 4.0 1260759205
## 6 1 1263 2.0 1260759151How would their combination look like? lets combine the two datasets into one using “user” as a link.
combined_dataset <- merge(ratings_df, movies_df,by="user",na.rm=TRUE)
kable(head(combined_dataset))| user | item | rating | timestamp | title | genres |
|---|---|---|---|---|---|
| 1 | 3671 | 3.0 | 1260759117 | Toy Story (1995) | Adventure|Animation|Children|Comedy|Fantasy |
| 1 | 2968 | 1.0 | 1260759200 | Toy Story (1995) | Adventure|Animation|Children|Comedy|Fantasy |
| 1 | 1061 | 3.0 | 1260759182 | Toy Story (1995) | Adventure|Animation|Children|Comedy|Fantasy |
| 1 | 2105 | 4.0 | 1260759139 | Toy Story (1995) | Adventure|Animation|Children|Comedy|Fantasy |
| 1 | 1293 | 2.0 | 1260759148 | Toy Story (1995) | Adventure|Animation|Children|Comedy|Fantasy |
| 1 | 2455 | 2.5 | 1260759113 | Toy Story (1995) | Adventure|Animation|Children|Comedy|Fantasy |
uirt <- combined_dataset[, c(1, 2,3,5)]
kable(head(uirt))| user | item | rating | title |
|---|---|---|---|
| 1 | 3671 | 3.0 | Toy Story (1995) |
| 1 | 2968 | 1.0 | Toy Story (1995) |
| 1 | 1061 | 3.0 | Toy Story (1995) |
| 1 | 2105 | 4.0 | Toy Story (1995) |
| 1 | 1293 | 2.0 | Toy Story (1995) |
| 1 | 2455 | 2.5 | Toy Story (1995) |
Here, we can see clearly that correlation may not be suitable to
cor(uirt$item, y =uirt$user , use = "everything",method = c("pearson", "kendall", "spearman"))## [1] -0.01738788Reading the datasets into Spark environment.
spark_rating <- spark_read_csv(sc, "spark_rating", "C:/Users/mayowa/Downloads/ml-latest-small/ml-latest-small/ratings.csv", header = TRUE,infer_schema = TRUE,charset = "UTF-8", null_value = NULL,repartition = 0, memory = TRUE, overwrite = TRUE)
spark_movies <- spark_read_csv(sc, "spark_movies", "C:/Users/mayowa/Downloads/ml-latest-small/ml-latest-small/movies.csv", header = TRUE,infer_schema = TRUE,charset = "UTF-8", null_value = NULL,repartition = 0, memory = TRUE, overwrite = TRUE)
class(spark_rating) # The class is now spark table,sql table etc## [1] "tbl_spark" "tbl_sql" "tbl_lazy" "tbl"#class(spark_rating)Is it actually a SQL table? Lets check…
suppressMessages(library(DBI))
my_table <- DBI::dbGetQuery(sc , "SELECT * FROM spark_rating LIMIT 10")
kable(my_table)| userId | movieId | rating | timestamp |
|---|---|---|---|
| 1 | 31 | 2.5 | 1260759144 |
| 1 | 1029 | 3.0 | 1260759179 |
| 1 | 1061 | 3.0 | 1260759182 |
| 1 | 1129 | 2.0 | 1260759185 |
| 1 | 1172 | 4.0 | 1260759205 |
| 1 | 1263 | 2.0 | 1260759151 |
| 1 | 1287 | 2.0 | 1260759187 |
| 1 | 1293 | 2.0 | 1260759148 |
| 1 | 1339 | 3.5 | 1260759125 |
| 1 | 1343 | 2.0 | 1260759131 |
Spark Test & Train Dataset.
To implement a model build up, we will train our dataset and validate it using test dataset. This can be done by splitting them into test and train.
The train dataset would take 80% of the dataset, while the remaing 20% goes to test.
spark_rating <- select(spark_rating, -timestamp)
spark_train <- sdf_sample(spark_rating, fraction = 0.8, replacement = FALSE, seed = 123)
spark_test <- sdf_sample(spark_rating, fraction = 0.2, replacement = FALSE, seed = 123)
kable(head(spark_train))| userId | movieId | rating |
|---|---|---|
| 1 | 31 | 2.5 |
| 1 | 1172 | 4.0 |
| 1 | 1263 | 2.0 |
| 1 | 1287 | 2.0 |
| 1 | 1293 | 2.0 |
| 1 | 1343 | 2.0 |
kable(head(spark_test))| userId | movieId | rating |
|---|---|---|
| 1 | 1287 | 2 |
| 1 | 1343 | 2 |
| 1 | 2105 | 4 |
| 1 | 3671 | 3 |
| 2 | 153 | 4 |
| 2 | 185 | 3 |
Or this method.
partition <- sdf_partition(spark_rating,training=0.8, testing=0.2)
sdf_register(partition,c("train_spark","test_spark"))## $train_spark
## Source: query [8e+04 x 3]
## Database: spark connection master=local[4] app=sparklyr local=TRUE
##
## userId movieId rating
## <int> <int> <dbl>
## 1 1 31 2.5
## 2 1 1061 3.0
## 3 1 1172 4.0
## 4 1 1263 2.0
## 5 1 1287 2.0
## 6 1 1293 2.0
## 7 1 1339 3.5
## 8 1 1343 2.0
## 9 1 1371 2.5
## 10 1 1953 4.0
## # ... with 7.999e+04 more rows
##
## $test_spark
## Source: query [2e+04 x 3]
## Database: spark connection master=local[4] app=sparklyr local=TRUE
##
## userId movieId rating
## <int> <int> <dbl>
## 1 1 1029 3.0
## 2 1 1129 2.0
## 3 1 1405 1.0
## 4 1 2150 3.0
## 5 1 2455 2.5
## 6 2 150 5.0
## 7 2 153 4.0
## 8 2 161 3.0
## 9 2 235 3.0
## 10 2 261 4.0
## # ... with 1.999e+04 more rowstest_rating <- tbl(sc,"test_spark")
tab <- tbl(sc,"train_spark") %>% select(userId, movieId, rating)
rating_model <- tab %>% ml_decision_tree(response="rating", features=c("userId","movieId"))## * No rows dropped by 'na.omit' callSpark Ratings prediction
A side-by-side glipmse of original ratings values and predicted values using spark
pred_spark <- sdf_predict(rating_model, test_rating) %>% collect
kable(head(pred_spark))| userId | movieId | rating | prediction |
|---|---|---|---|
| 1 | 1029 | 3.0 | 3.769757 |
| 1 | 1129 | 2.0 | 3.769757 |
| 1 | 1405 | 1.0 | 3.380091 |
| 1 | 2150 | 3.0 | 3.846154 |
| 1 | 2455 | 2.5 | 3.163677 |
| 2 | 150 | 5.0 | 3.334603 |
The above table shows side-by-side prediction of the ratings.
sqrt(mean(with(pred_spark, pred_spark)^2))## [1] 14513.99spark_disconnect(sc) # Disconnecting sparkNow on base R
genre_list <- c("Action", "Adventure", "Animation", "Children", "Comedy", "Crime","Documentary", "Drama", "Fantasy","Film-Noir", "Horror", "Musical", "Mystery","Romance","Sci-Fi", "Thriller", "War", "Western")
mat_genre <- matrix(0,8571,18) #empty matrix
mat_genre[1,] <- genre_list #set first row to genre list
colnames(mat_genre) <- genre_list #set column names to genre list
#convert into dataframe and removing the first row,that was in genres list above
mat_genre_2 <- as.data.frame(mat_genre[-1,], stringsAsFactors=FALSE)
for (c in 1:ncol(mat_genre_2)) {
mat_genre_2[,c] <- as.integer(mat_genre_2[,c])
}Base R splitting of train and test dataset
set.seed(123)
ratings_df[is.na(ratings_df)] <- 0
samp_data <- ratings_df[sample(nrow(ratings_df)),]
samp_sub<- samp_data[,-c(4)]
train_dat <- subset(samp_sub[1:80003, ]) #80 percent
test_dat <- subset(samp_sub[80004:100004, ]) #20 percentTest and train data structures.
str(ratings_df)# Note the ommision of timestamp## 'data.frame': 100004 obs. of 4 variables:
## $ user : int 1 1 1 1 1 1 1 1 1 1 ...
## $ item : int 31 1029 1061 1129 1172 1263 1287 1293 1339 1343 ...
## $ rating : num 2.5 3 3 2 4 2 2 2 3.5 2 ...
## $ timestamp: int 1260759144 1260759179 1260759182 1260759185 1260759205 1260759151 1260759187 1260759148 1260759125 1260759131 ...str(train_dat)## 'data.frame': 80003 obs. of 3 variables:
## $ user : int 212 547 294 587 624 23 384 595 396 326 ...
## $ item : int 2915 1569 6296 1269 3255 5952 2000 1220 292 3308 ...
## $ rating: num 3 3.5 4.5 4.5 4 4 4 4 4 3 ...str(test_dat)## 'data.frame': 20001 obs. of 3 variables:
## $ user : int 647 664 316 486 262 203 509 367 388 451 ...
## $ item : int 2916 8528 6378 541 4914 913 3095 4963 1485 2858 ...
## $ rating: num 3 3.5 3 5 3.5 4 4 3 4 4 ...Below we will reshape2 package to cast the train dataset, convert it to matrix first and later to recommerlab “realRatingMatrix”, normalized it and a visual representation of it (plot).
train_dat_casting <- acast(data=train_dat,train_dat$userId~train_dat$movieId)## Using rating as value column: use value.var to override.## Aggregation function missing: defaulting to lengthrating_matx <- as.matrix(train_dat_casting)
rating_ind <- as(rating_matx,"realRatingMatrix")
head(as(rating_ind ,"list"),1)## $.
## .
## 80003head(as(rating_ind ,"data.frame"),1)## user item rating
## 1 . . 80003image(rating_ind,main="Raw Ratings")#Raw--->Non-Normalized
rating_norm <-normalize(rating_ind)
image(rating_norm,main="Normalized Ratings")#Normalized
On movies dataset
# let us vectorised the movies_df
ratings_vec <- as.vector(movies_df)
#unique(ratings_vec)
ratings_vec <- ratings_vec[ratings_vec != 0]
ratings_vec <- factor(ratings_vec)
head(ratings_vec)## [1] 1 2 3 4 5 6
## 19150 Levels: 'burbs, The (1989) ... Zulu (2013)test_data_mat <- as(test_dat, "realRatingMatrix")
test_dat_bin <- as(test_dat, "binaryRatingMatrix")library(recommenderlab)
movies_RRM <- as(movies_df, "realRatingMatrix")
views_per_movie <- colCounts(movies_RRM)
table_views <- data.frame(
movie = names(views_per_movie),
views = views_per_movie
)
table_views <- table_views[order(table_views$views, decreasing =
TRUE), ]
ggplot(table_views[1:7, ], aes(x = movie, y = views)) +
geom_bar(stat="identity") + theme(axis.text.x =
element_text(angle = 25, hjust = 1)) + ggtitle("Number of views
of the top movies")
The above plot shows the top number of movies watched since 1989 to year 2005.
Cosine Similarity :
Thisis a measure of similarity between two non-zero vectors of an inner product space that measures the cosine of the angle between them.
$Similarity=cos()= $
users_sim <- similarity(movies_RRM[1:2500, ], method =
"cosine", which = "users")
image(as.matrix(users_sim), main = "Similarity in Users")
ratings_matrix <- as(train_dat, "binaryRatingMatrix")
number_of_users <- colCounts(ratings_matrix);
qplot(number_of_users) + stat_bin(binwidth = 20) + ggtitle("Distribution of the number of users");## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
model2 <- Recommender(data = ratings_matrix, method = "UBCF", parameter = list(method = "Jaccard"))test_data_mat <- as(test_dat, "realRatingMatrix")
test_dat_bin <- as(test_dat, "binaryRatingMatrix")
recomm <- Recommender(test_data_mat[1:nrow(test_data_mat)],method="UBCF",
param=list(normalize="Z-score",method="Cosine"
,nn=5,minRating=1))## Available parameter (with default values):
## method = cosine
## nn = 25
## sample = FALSE
## normalize = center
## verbose = FALSErecommed <-predict(recomm,test_data_mat[1:nrow(test_data_mat)],type="ratings")
recommed_topN <-predict(recomm,test_data_mat[1:nrow(test_data_mat)],type="topNList",n=10)head(as(recommed_topN,"list"),5)## $`1`
## [1] "2028" "922" "1197" "2959" "59369" "89745" "1663" "2109"
## [9] "2947" "6058"
##
## $`2`
## [1] "318" "110" "527" "5989" "8529" "79132" "63082" "84152"
## [9] "1" "2"
##
## $`3`
## [1] "356" "1580" "539" "2" "349" "364" "593" "597" "661" "1028"
##
## $`4`
## [1] "39" "1682" "377" "1545" "296" "349" "457" "236" "356" "357"
##
## $`5`
## [1] "337" "471" "562" "898" "912" "922" "930" "1029" "1057" "1175"qplot(getRatings(test_data_mat),binwidth=1,col="blue",main="Ratings Plot",xlab="Ratings")
hist(getRatings(test_data_mat),binwidth=1,col="black",breaks=15,main="Histogram of Ratings",xlab="Ratings");
qplot(getRatings(rating_norm),binwidth=1,main="Normalized Ratings",xlab="Ratings")
qplot(rowCounts(test_data_mat),binwidth=10,main="Movie Average Ratings",xlab="Number of users",ylab = "Number of movie ratings")
qplot(colMeans(test_data_mat),binwidth=.1,main=" Mean Ratings ",xlab="Ratings",ylab = "Number of movies")
rating_bin <- binarize(test_data_mat,minRating=1)
head(as(rating_bin,"matrix"),1)## 1 2 3 4 5 6 7 8 9 10 11 12
## 1 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## 13 14 15 16 17 18 19 20 21 22 23 24
## 1 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## 25 26 27 28 29 30 31 32 34 35 36 39
## 1 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## 40 41 42 43 44 45 46 47 48 50 52 55
## 1 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## 57 58 59 60 61 62 65 66 68 69 70 71
## 1 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## 73 74 76 78 79 80 81 82 83 85 86 87
## 1 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## 88 89 92 94 95 96 97 100 101 102 103 104
## 1 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## 105 107 110 111 112 113 117 122 123 125 126 132
## 1 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## 135 140 141 144 145 147 149 150 151 152 153 154
## 1 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## 155 156 158 159 160 161 162 163 164 165 168 169
## 1 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## 170 171 172 173 175 176 177 179 180 181 185 186
## 1 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## 188 189 191 193 194 195 196 198 203 204 205 207
## 1 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## 208 209 213 215 216 217 218 220 222 223 224 225
## 1 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## 227 229 230 231 232 233 234 235 236 237 238 239
## 1 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## 240 242 243 246 247 248 249 250 252 253 254 256
## 1 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## 257 258 259 260 261 262 265 266 267 269 270 271
## 1 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## 272 273 275 276 277 278 279 280 281 282 287 288
## 1 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## 289 290 292 293 294 295 296 299 300 302 303 304
## 1 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## 305 306 307 308 312 313 314 315 316 317 318 319
## 1 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## 320 321 322 324 326 327 328 329 330 332 333 334
## 1 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
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## 1 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## 349 350 351 352 353 355 356 357 358 360 361 362
## 1 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## 363 364 365 366 367 368 370 371 372 373 374 375
## 1 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## 376 377 378 379 380 381 382 383 384 387 391 393
## 1 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## 405 407 408 409 410 412 413 414 415 417 418 419
## 1 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
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## 1 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
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## 1 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## 450 451 452 454 455 456 457 458 459 460 463 464
## 1 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
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## 1 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
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## 1 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## 496 497 500 501 502 504 505 506 507 508 509 511
## 1 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
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## 1 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## 527 529 531 532 534 535 536 537 538 539 540 541
## 1 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## 542 543 544 546 548 549 550 551 552 553 555 556
## 1 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## 558 559 562 567 569 575 581 585 586 587 588 589
## 1 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
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## 1 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## 610 611 612 613 614 616 619 627 628 630 631 633
## 1 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## 637 638 639 640 647 648 650 653 661 663 665 667
## 1 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## 671 673 674 678 679 681 685 688 691 694 695 697
## 1 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## 698 700 703 704 707 708 709 711 714 715 719 720
## 1 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## 724 728 733 735 736 737 741 742 743 745 747 748
## 1 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## 750 761 762 765 766 778 779 780 781 783 784 785
## 1 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## 786 788 798 799 800 801 802 803 804 805 806 808
## 1 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## 809 810 813 816 818 820 828 830 831 832 835 837
## 1 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
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## 1 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
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## 1 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
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## 1 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
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## 1 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## 932 933 934 935 936 937 938 940 942 943 944 945
## 1 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
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## 1 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
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## 1 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## 996 998 999 1003 1004 1005 1007 1008 1009 1012 1013 1014
## 1 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## 1015 1016 1017 1018 1019 1020 1021 1022 1023 1025 1027 1028
## 1 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## 1029 1030 1031 1032 1033 1034 1035 1036 1037 1041 1042 1043
## 1 TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
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## 1 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## 1063 1064 1066 1068 1073 1077 1078 1079 1080 1081 1082 1083
## 1 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## 1084 1085 1086 1087 1088 1089 1090 1091 1092 1093 1094 1095
## 1 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## 1096 1097 1099 1100 1101 1103 1104 1105 1112 1114 1120 1124
## 1 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## 1125 1126 1127 1128 1129 1130 1131 1132 1133 1135 1136 1147
## 1 FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## 1148 1150 1161 1162 1169 1171 1172 1173 1175 1176 1177 1178
## 1 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## 1179 1181 1183 1184 1185 1186 1187 1188 1189 1190 1191 1193
## 1 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
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## 1 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
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## 1 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
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## 1 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
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## 1 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## 1246 1247 1248 1249 1250 1251 1252 1253 1254 1255 1256 1257
## 1 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
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## 1 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
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## 1 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## 1282 1283 1284 1285 1286 1287 1288 1289 1290 1291 1292 1293
## 1 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
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## 1 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## 1307 1310 1320 1321 1326 1327 1332 1333 1334 1335 1337 1339
## 1 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## 1340 1342 1343 1344 1345 1346 1347 1348 1349 1350 1352 1353
## 1 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## 1354 1355 1356 1357 1358 1359 1361 1363 1365 1366 1367 1370
## 1 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## 1371 1372 1373 1374 1375 1376 1377 1378 1379 1380 1381 1385
## 1 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
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## 1 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE
## 1406 1407 1408 1409 1410 1411 1413 1414 1416 1422 1425 1429
## 1 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
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## 1 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
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## 1 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
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## 1 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
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## 1 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
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## 1 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
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## 1 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
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## 1 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSErecomm_IBCF <- Recommender(test_data_mat[1:nrow(test_data_mat)],method="IBCF",
param=list(normalize="Z-score",method="Cosine"
,nn=5,minRating=1))## Available parameter (with default values):
## k = 30
## method = Cosine
## normalize = center
## normalize_sim_matrix = FALSE
## alpha = 0.5
## na_as_zero = FALSE
## verbose = FALSEcomb_RRM <- as(combined_dataset, "realRatingMatrix")
movies_ratings <- comb_RRM[rowCounts(comb_RRM) > 50,colCounts(comb_RRM) > 100]Bootstrapping
We will use bootstrapping to sample same user more than once, so that we make have more user(s) to be tested.
eval_scheme <- evaluationScheme(data = movies_ratings, method = "bootstrap", train = 0.8, given = 2, goodRating =3, k = 1)Evaluating and Prediction
eval_recomm <- Recommender(data = getData(eval_scheme, "train"),method = "IBCF", parameter = NULL)eval_prediction <- predict(object = eval_recomm, newdata =getData(eval_scheme, "known"), n = 10, type = "ratings")qplot(rowCounts(eval_prediction)) + geom_histogram(binwidth = 10) + ggtitle("Distribution of movies per user")## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Dimensionality Reduction
Root Mean Square Error (RMSE): This is frequently used measure of the differences between values (sample and population values) predicted by a model or an estimator and the values actually observed. i.e used to measure the standard deviation between the real and predicted values.
\(\sqrt { \sum _{ t=1 }^{ n }{ { ({ \hat { y } }_{ t }-\quad { y }_{ t }\quad ) }^{ 2 }/\quad n } }\)
Mean Sqaure Error (MSE): This being a risk fuction is used to measures the average of the squares of the errors or deviations-that is, the difference between the mean of an estimator and what is estimated.
\(MSE\quad =\quad \frac { 1 }{ n } \sum _{ i=1 }^{ n }{ { (\quad \hat { { Y }_{ i } } -\quad { Y }_{ i }\quad ) }^{ 2 } }\)
Mean Absolute Error (MAE): As the name implies, it is the mean of absolute different between estimator and what is estimated.
\(MAE\quad =\quad \frac { \sum _{ i=1 }^{ n }{ |{ y }_{ i }\quad -\quad { x }_{ i }|\quad \quad \quad } }{ n }\)
eval_pred_accuracy <- calcPredictionAccuracy(x = eval_prediction, data = getData(eval_scheme, "unknown"), byUser =TRUE)
output <- evaluate(x = eval_scheme, method = "IBCF", n =seq(10, 100, 10))## IBCF run fold/sample [model time/prediction time]
## 1 [0.04sec/0.03sec]kable(head(eval_pred_accuracy))| RMSE | MSE | MAE | |
|---|---|---|---|
| 2 | NaN | NaN | NaN |
| 3 | 1.5448411 | 2.3865340 | 1.2607948 |
| 4 | NaN | NaN | NaN |
| 8 | 0.7316053 | 0.5352463 | 0.5555457 |
| 12 | 2.5495098 | 6.5000000 | 2.5000000 |
| 13 | 0.9258201 | 0.8571429 | 0.5714286 |
COnfusion Matrix & ROC Curve.
kable(head(getConfusionMatrix(output)[1]))
|
plot(output, annotate = TRUE, main = "ROC curve")
plot(output, "prec/rec", annotate = TRUE, main = "Precision-recall")
Conclusion:
From the analysis above, we can deduce that the recommendation system help the following:
Recommender system will always add company own marketing and inventory control directive to the customer’s profile to feature product that are promotionally prices, on clearance overstocked. It also enables flexibility to control what items are highlighted by the engine.
Recommender system also help to reduce the workload of IT department due to large data that may required to create a personal shopping experience.
It will make life easier for consumer of such products or services, because it will give them more time to do something else.
Recommendersystem will provide accurate and up to the minute report to both consumer/client and company about the site and directions.
References
- https://www.wikipedia.org/
- Building a Recommendation System with R by Suresh K. Gorakala & Michele Usuelli
- https://www.coursera.org/specializations/recommender-systems
- http://infolab.stanford.edu/~ullman/mmds/ch9.pdf