Final Project: Recommendation Systems
Moiya Josephs
5/12/2022
1 Overview
For the final project I took three data sets, all taken from Kaggle and used recommenderlab library on the data sets. Each data set allowed me to test the different recommendation methods and calculating the accuracy for each one/
The first data set is:
- Beauty Ratings from Amazon
- Netflix Ratings
- Book Ratings
2 Beauty Recommendation
For this part of my project I used the Amazon Beauty Rating data set from Kaggle. The data set has over 2 Million customer reviews and ratings of Beauty related products sold on their website. It isa subset of all their product reviews that span from May 1996 - July 2014.
The Schema
- UserId (Customer Identification)
- the product ASIN (Amazon’s unique product identification code for each product)
- Ratings (1-5 based on customer satisfaction)
- Timestamp (in UNIX time)
2.1 Reading the Data
ratings <- read.csv("https://raw.githubusercontent.com/moiyajosephs/Data-607-Projects/main/Final%20Project/ratings_Beauty.csv", header = TRUE, sep = ",", dec = ".", quote="")head(ratings)## UserId ProductId Rating Timestamp
## 1 A39HTATAQ9V7YF 0205616461 5 1369699200
## 2 A3JM6GV9MNOF9X 0558925278 3 1355443200
## 3 A1Z513UWSAAO0F 0558925278 5 1404691200
## 4 A1WMRR494NWEWV 0733001998 4 1382572800
## 5 A3IAAVS479H7M7 0737104473 1 1274227200
## 6 AKJHHD5VEH7VG 0762451459 5 1404518400summary(ratings)## UserId ProductId Rating Timestamp
## Length:2023070 Length:2023070 Min. :1.000 Min. :9.088e+08
## Class :character Class :character 1st Qu.:4.000 1st Qu.:1.350e+09
## Mode :character Mode :character Median :5.000 Median :1.373e+09
## Mean :4.149 Mean :1.360e+09
## 3rd Qu.:5.000 3rd Qu.:1.391e+09
## Max. :5.000 Max. :1.406e+092.2 Formatting the data
To make the dataframe into something a recommenderlab function can use, I had to go through a number of steps first.
2.2.1 Converting to factors
First, I checked the class for each column since sparce matrix and real matrix are very particular.
class(ratings$UserId); class(ratings$ProductId); class(ratings$Rating); class(ratings$Timestamp)## [1] "character"## [1] "character"## [1] "numeric"## [1] "integer"Changed the character columns to factors.
ratings$UserId<- as.factor(ratings$UserId);
ratings$ProductId <- as.factor(ratings$ProductId)2.2.2 Build a Sparce matrix
Convert into ratings matrix. This is done first by using the sparse matrix function. The rows will be the integer values of the UserId and the columns will be the ProductId. The x, values will be the ratings.
ratingsMatrix <- sparseMatrix(as.integer(ratings$UserId), as.integer(ratings$ProductId), x = ratings$Rating)Next use the colnames and rownames function on the matrix so that each cell is properly labelled.
colnames(ratingsMatrix) <- levels(ratings$ProductId)
rownames(ratingsMatrix) <- levels(ratings$UserId)2.2.3 Make a realRatingMatrix
Save the sparse matrix as a realRatingMatrix
beauty <- as(ratingsMatrix, "realRatingMatrix") #change to beauty2.3 Working with the realRatingMatrix
With the getRatingMatrix() function we can see the resulting matrix from the steps above.
getRatingMatrix(beauty)[1:10,1:4]## 10 x 4 sparse Matrix of class "dgCMatrix"
## 0205616461 0558925278 0733001998 0737104473
## A00008821J0F472NDY6A2 . . . .
## A000186437REL8X2RW8UW . . . .
## A0002574WYJMBWKNCPY8 . . . .
## A00029263J863WSR0TDRS . . . .
## A00031961JI1CBNV98TW . . . .
## A000325234LCBTFVL1QK4 . . . .
## A0003526DBE3EVU1DXE5 . . . .
## A0004690232S2SX49HSDW . . . .
## A000622834IP6B056754X . . . .
## A0007094NAUEOIOA53YH . . . .The getRatings() function allows us to see the just the ratings. It is also a good way to check if the realRatingMatrix identified the right numbers as ratings.
Representing visually
data.frame(ratings = getRatings(beauty)) %>%
ggplot(aes(ratings)) + geom_bar(width = 0.75)
The graph above shows that 5 star ratings are the most frequently chosen rating for the products.
2.4 Readying the data for recommender models
The rowcounts/colcounts function allows us to view the unique number of unique row/ col values . This is important with a ratingmatrix and for recommendation system because there are some users/products with only one rating. This is not enough data to make predictions on.
table(rowCounts(beauty))##
## 1 2 3 4 5 6 7 8 9 10 11
## 887401 175875 64336 30285 16187 9827 6324 4260 3181 2275 1745
## 12 13 14 15 16 17 18 19 20 21 22
## 1402 1012 912 677 550 462 411 323 277 262 238
## 23 24 25 26 27 28 29 30 31 32 33
## 198 160 132 105 129 95 94 82 68 53 64
## 34 35 36 37 38 39 40 41 42 43 44
## 51 49 45 32 41 37 38 30 35 29 20
## 45 46 47 48 49 50 51 52 53 54 55
## 13 25 25 19 19 17 13 18 13 15 14
## 56 57 58 59 60 61 62 63 64 65 66
## 11 15 6 6 13 6 9 11 3 10 4
## 67 68 69 70 71 72 73 74 75 76 77
## 4 5 6 6 5 2 6 5 7 5 5
## 78 79 80 81 82 83 84 85 86 87 88
## 2 3 2 8 3 1 5 1 2 4 1
## 89 90 91 92 93 94 95 96 97 98 99
## 2 3 1 2 5 2 3 1 1 2 2
## 102 103 104 105 107 108 109 110 112 113 114
## 2 1 3 1 1 1 3 1 2 1 2
## 115 116 117 118 120 122 125 127 129 130 131
## 1 1 2 2 1 1 1 2 1 1 1
## 132 134 135 137 139 141 145 150 151 152 154
## 1 1 2 1 1 1 1 1 1 1 1
## 155 164 168 170 172 173 182 186 205 209 211
## 1 1 1 1 1 1 1 1 1 1 1
## 225 249 259 269 275 276 278 326 336 389
## 1 1 1 1 1 1 1 1 1 1table(colCounts(beauty))##
## 1 2 3 4 5 6 7 8 9 10 11
## 103484 42209 22334 13902 9623 7214 5592 4404 3574 3059 2542
## 12 13 14 15 16 17 18 19 20 21 22
## 2267 2024 1657 1526 1410 1208 1096 1054 912 869 810
## 23 24 25 26 27 28 29 30 31 32 33
## 723 663 591 545 508 517 468 432 381 388 361
## 34 35 36 37 38 39 40 41 42 43 44
## 338 332 347 276 269 267 261 261 245 221 226
## 45 46 47 48 49 50 51 52 53 54 55
## 177 204 188 177 167 174 156 138 151 157 132
## 56 57 58 59 60 61 62 63 64 65 66
## 142 112 119 105 120 103 125 116 111 83 94
## 67 68 69 70 71 72 73 74 75 76 77
## 99 86 89 92 89 78 84 85 79 74 66
## 78 79 80 81 82 83 84 85 86 87 88
## 66 63 55 72 60 62 62 58 51 64 56
## 89 90 91 92 93 94 95 96 97 98 99
## 52 61 56 50 47 41 36 49 46 49 37
## 100 101 102 103 104 105 106 107 108 109 110
## 32 34 39 40 26 46 46 34 36 27 29
## 111 112 113 114 115 116 117 118 119 120 121
## 32 39 26 25 29 33 28 22 23 19 27
## 122 123 124 125 126 127 128 129 130 131 132
## 22 21 28 24 16 24 25 18 19 20 16
## 133 134 135 136 137 138 139 140 141 142 143
## 24 27 21 15 31 21 23 14 9 16 19
## 144 145 146 147 148 149 150 151 152 153 154
## 17 24 15 8 16 10 11 13 16 11 15
## 155 156 157 158 159 160 161 162 163 164 165
## 16 5 18 16 13 12 6 10 14 16 14
## 166 167 168 169 170 171 172 173 174 175 176
## 17 10 19 18 13 8 8 10 11 4 13
## 177 178 179 180 181 182 183 184 185 186 187
## 12 7 11 14 10 9 9 8 12 9 10
## 188 189 190 191 192 193 194 195 196 197 198
## 11 9 15 6 5 5 8 8 7 7 12
## 199 200 201 202 203 204 205 206 207 208 209
## 9 6 4 8 4 5 12 9 8 5 4
## 210 211 212 213 214 215 216 217 218 219 220
## 10 6 5 10 9 8 6 5 10 6 4
## 221 222 223 224 225 226 227 228 229 230 231
## 4 4 5 9 5 1 5 5 9 7 8
## 232 233 234 235 236 237 238 239 240 241 242
## 1 8 7 11 8 4 4 4 7 7 5
## 243 244 245 246 247 248 249 250 251 252 253
## 4 6 3 2 3 7 5 5 5 3 7
## 254 255 256 257 258 259 260 261 262 263 264
## 3 8 8 4 5 2 7 4 3 3 6
## 265 266 267 268 269 272 273 274 275 276 277
## 5 3 3 3 3 4 2 2 1 5 2
## 278 279 281 282 283 284 285 286 287 288 289
## 3 3 7 4 2 2 4 3 3 4 6
## 290 291 292 293 294 295 296 297 298 299 300
## 3 2 3 3 3 5 2 2 4 1 6
## 301 302 303 305 306 307 308 309 310 311 312
## 3 2 3 4 3 4 1 3 1 5 3
## 313 315 316 318 319 320 321 322 324 326 327
## 3 5 4 2 3 1 4 2 1 1 3
## 328 329 330 331 332 333 334 335 336 337 338
## 1 1 5 2 2 2 1 2 3 3 3
## 339 340 341 342 345 346 347 348 349 350 351
## 5 2 3 1 2 1 1 2 1 3 2
## 352 353 354 355 358 359 360 362 363 364 366
## 1 1 2 6 2 1 3 3 2 2 1
## 368 369 370 372 375 376 377 379 380 381 383
## 3 3 3 2 1 2 5 2 2 1 1
## 384 386 387 388 389 391 392 394 395 396 397
## 1 2 3 1 1 1 1 1 1 1 1
## 398 399 400 402 404 405 406 407 409 411 412
## 3 2 1 1 1 1 2 1 1 1 4
## 413 414 415 416 418 419 422 423 426 427 429
## 2 3 3 1 2 2 1 2 1 1 1
## 430 431 432 434 435 436 438 441 442 443 446
## 3 1 2 1 1 1 3 1 2 2 2
## 447 452 455 456 458 459 460 461 462 463 465
## 2 2 1 1 1 2 3 2 1 3 2
## 466 467 468 472 473 474 478 479 480 481 483
## 1 1 3 1 1 3 1 1 4 1 1
## 488 489 494 495 496 497 498 499 500 501 502
## 1 1 1 2 2 1 2 1 2 3 1
## 503 506 507 509 510 511 512 513 514 515 519
## 2 1 2 1 3 1 2 1 1 1 1
## 520 523 524 534 537 539 544 545 550 551 553
## 1 1 3 1 1 1 1 2 2 1 1
## 554 557 558 563 565 581 584 585 587 590 591
## 1 2 3 2 1 1 1 2 1 1 1
## 594 595 597 598 599 600 601 605 607 609 614
## 1 1 1 1 2 1 1 1 1 1 1
## 616 618 619 624 639 643 644 653 656 661 662
## 1 1 1 1 2 1 1 1 1 1 1
## 665 666 668 671 672 678 680 682 685 686 687
## 1 1 1 1 1 1 1 1 1 1 2
## 689 691 698 700 704 706 707 709 713 714 720
## 1 1 1 1 1 1 1 1 2 1 1
## 725 729 734 735 736 738 746 755 757 758 765
## 1 1 1 1 1 1 1 1 1 1 1
## 766 768 773 782 784 789 795 798 810 818 828
## 1 1 2 1 1 1 1 1 1 1 1
## 834 845 865 880 883 885 888 896 899 925 926
## 1 1 1 1 1 1 1 1 1 1 1
## 943 945 946 981 992 1046 1051 1061 1074 1079 1105
## 1 1 1 1 1 1 1 1 1 1 1
## 1108 1135 1136 1153 1159 1163 1323 1330 1333 1341 1347
## 1 1 1 1 1 1 1 1 1 1 1
## 1349 1379 1430 1468 1475 1558 1589 1838 1885 1918 2041
## 2 1 1 1 1 1 1 1 1 1 1
## 2088 2143 2477 2869 7533
## 1 1 1 1 1Limit the matrix to only include values with more than 30 column count and 10 row counts.
beauty <- beauty[,colCounts(beauty) >30]beauty <- beauty[rowCounts(beauty) > 10, ]getRatingMatrix(beauty)[1:10,1:4]## 10 x 4 sparse Matrix of class "dgCMatrix"
## 7806397051 9746427962 9759091062 9788071198
## A02155413BVL8D0G7X6DN . . . .
## A03364251DGXSGA9PSR99 . . . .
## A099766128UI0NCS98N1E . . . .
## A100WO06OQR8BQ . . . .
## A100ZQDV7L8PVV . . . .
## A1022QCIW8Z720 . . . .
## A103979529MRJY0U56QI4 . . . .
## A103BJIOJSDJL1 . . . .
## A103WXT3CHVY0H . . . .
## A1047EDJ84IMAS . . . .data.frame(ratings = getRatings(beauty)) %>%
ggplot(aes(ratings)) + geom_bar(width = 0.75)
As we can see the amount of ratings has decreased but ratings = 5 are still the most popular. Now the data is ready for recommendation model
2.5 Build the model
To use a machine learning model we need to divide the data into training, known and unknown data. We will build the model on training data, and predict on the known data. Next we can calculate accuracy with the unknown data.
Below I decided on dividing the data so that 75% of the data is training data. I also need to note the minimum amount of rowcounts, the number of occurrences of users, available. This is important for later.
train_proportion <- .75
min(rowCounts(beauty))## [1] 11Below the given value is what we found as the minimum rowcounts. In the evaluationscheme, , the given parameter cannot be greater than the minimum rowcounts. The goodrating value gives the model the rating we should be considering as good. Since the data ratings ranges from 1-5 , I indicate that a good rating is anything that is 4 or above.
give <- 11
# What's a good rating for a binary split?
goodrating <- 4# Building a Recommender System with R by Gorakala and Usuelli. Ch.4 pp 77 - 83
set.seed(12345)
beauty_evaluation <- beauty %>% evaluationScheme(method = 'split', # single train/test split
train = train_proportion, # proportion of rows to train.
given = give, # shouldn't keep n rec. items > min(rowCounts(movie_r))
goodRating = goodrating, # for binary classifier analysis.
k = 1)Modelparams builds the parameters for the recommended function. The method in the parameters can be cosine, ,. nn indicates to find the n most similar users. And we can also sample our data or normalized.I normalized thed data since user rating can be biased, normalizing th data will help make it fair. The example I used to make the modelparams is from the tutorial .
# Building a Recommender System with R by Gorakala and Usuelli. Ch.4 pp 84
model_params <- list(method = "cosine",
nn = 10, # find each user's 10 most similar users.
sample = FALSE, # already did this.
normalize = "center")2.6 Divide the data
set.seed(88)
train <- getData(beauty_evaluation, "train")
known <- getData(beauty_evaluation, "known")
unknown <- getData(beauty_evaluation, "unknown")2.6.1 Popular
The popular method in recommender lab allows you to make recommendations based on the other popularly rated items. It is a popular recommendation method implementation. First we train on the training data, predict on the known and calculate accuracy on the unknown.
model_method <- "POPULAR"
# Training
modelPOP <- Recommender(train, method = model_method)predPOP <- predict(modelPOP, newdata = known, type = "ratings")
# Accuracy
accPOP <- calcPredictionAccuracy(predPOP, unknown)accPOP## RMSE MSE MAE
## 1.092789 1.194187 0.815674Get the top beatuy products
getModel(modelPOP)## $topN
## Recommendations as 'topNList' with n = 12057 for 1 users.
##
## $ratings
## 1 x 12057 rating matrix of class 'realRatingMatrix' with 8927 ratings.
## Normalized using center on rows.
##
## $normalize
## [1] "center"
##
## $aggregationRatings
## standardGeneric for "colMeans" defined from package "base"
##
## function (x, na.rm = FALSE, dims = 1, ...)
## standardGeneric("colMeans")
## <bytecode: 0x0000000023632d38>
## <environment: 0x0000000022f3b0e0>
## Methods may be defined for arguments: x
## Use showMethods(colMeans) for currently available ones.
##
## $aggregationPopularity
## standardGeneric for "colSums" defined from package "base"
##
## function (x, na.rm = FALSE, dims = 1, ...)
## standardGeneric("colSums")
## <bytecode: 0x00000000236312e8>
## <environment: 0x0000000023100900>
## Methods may be defined for arguments: x
## Use showMethods(colSums) for currently available ones.
##
## $verbose
## [1] FALSEgetModel(modelPOP)$topN## Recommendations as 'topNList' with n = 12057 for 1 users.The result contains two ordered top-N recommendation lists, one for each user. The recommended items can be inspected as a list.
recom <- predict(modelPOP, known, n=3)
head(as(recom,"list"))## $A103979529MRJY0U56QI4
## [1] "B00639DLV2" "B009FKNGRA" "B009YSSLAU"
##
## $A105S56ODHGJEK
## [1] "B00639DLV2" "B009FKNGRA" "B009YSSLAU"
##
## $A10UCR7QHTF29U
## [1] "B00639DLV2" "B009FKNGRA" "B009YSSLAU"
##
## $A110OOYMFXQPDA
## [1] "B00639DLV2" "B009FKNGRA" "B009YSSLAU"
##
## $A11BCJJG91KVOY
## [1] "B00639DLV2" "B009FKNGRA" "B009YSSLAU"
##
## $A11F3YONMF67ML
## [1] "B00639DLV2" "B009FKNGRA" "B009YSSLAU"recom3 <- bestN(recom, n = 3)
head(as(recom3, "list"))## $A103979529MRJY0U56QI4
## [1] "B00639DLV2" "B009FKNGRA" "B009YSSLAU"
##
## $A105S56ODHGJEK
## [1] "B00639DLV2" "B009FKNGRA" "B009YSSLAU"
##
## $A10UCR7QHTF29U
## [1] "B00639DLV2" "B009FKNGRA" "B009YSSLAU"
##
## $A110OOYMFXQPDA
## [1] "B00639DLV2" "B009FKNGRA" "B009YSSLAU"
##
## $A11BCJJG91KVOY
## [1] "B00639DLV2" "B009FKNGRA" "B009YSSLAU"
##
## $A11F3YONMF67ML
## [1] "B00639DLV2" "B009FKNGRA" "B009YSSLAU"2.6.2 Random
model_method <- "RANDOM"
# Training
modelRND <- Recommender(train, method = model_method)predRND <- predict(modelRND, newdata = known, type = "ratings")
# Accuracy
accRND <- calcPredictionAccuracy(predRND, unknown)accRND## RMSE MSE MAE
## 1.3325660 1.7757322 0.96240692.7 Conclusions
In conclusion the popular method was able to predict with a better accuracy than the random method.
3 Netflix
Netflix data was a little more difficult to get in the format needed to make the recommendation engine. Each movie was listed in the below format on 4 different files. Movie: customerID, Rating, Time There were # movies across multiple files.
To get each individual movie I made a python script that will read the text file and at each :, this will indicate a new movies ratings. With each new movie it will become its own file. With future iterations of this project I would like to use all the files, but for this project I will test the first 10 movies. This limits the scale and accuracy of the recommendation engine since the training data is a much smaller subset of the larger one.
3.1 Reading in each movie
Fist I read the text files individually. Next I remove the timestamp column and add in the movie id as a column, so we know which rating belongs to which movie.
movie1<- read.delim("https://raw.githubusercontent.com/moiyajosephs/Data-607-Projects/main/Final%20Project/movie-data/2.txt", header = FALSE, sep = ",", dec = ".")
# add movie id
movie1<- movie1[,c(1,2)]
movie_id <- rep(c(1),547)
movie1<- cbind(movie_id,movie1)movie_id <- rep(c(2),145)
movie2<- read.delim("https://raw.githubusercontent.com/moiyajosephs/Data-607-Projects/main/Final%20Project/movie-data/3.txt", header = FALSE, sep = ",", dec = ".")
movie2<- movie2[,1:2]
movie2<- cbind(movie_id,movie2)movie_id <- rep(c(3),2012)
movie3<- read.delim("https://raw.githubusercontent.com/moiyajosephs/Data-607-Projects/main/Final%20Project/movie-data/4.txt", header = FALSE, sep = ",", dec = ".")
movie3<- movie3[,1:2]
movie3<- cbind(movie_id,movie3)Using rbind, I can combine all the movies into one.
movies<- rbind(movie1, movie2, movie3)dim(movies)## [1] 2704 3Dcast Allows us to pivot the user id to the rows and the movie id to the columns, making it into a matrix like structure.
ratingsMatrix <- dcast(movies, V1~movie_id, value.var = "V2" )Again we subtract the user id from actual data, since it will confuse recommenderlab functions and be counted as a rating.
ratingsMatrix <- as.matrix(ratingsMatrix[-c(1)])3.2 Real Rating Matrix
Now we can coerce the matrix into a realRatingMatrix.
# Convert rating matrix into a recommenderlab sparse matrix
ratingsMatrix <- as(ratingsMatrix, "realRatingMatrix")
ratingsMatrix## 2674 x 3 rating matrix of class 'realRatingMatrix' with 2704 ratings.Now to display the ratings matrix.
getRatingMatrix(ratingsMatrix)[1:10,1:3]## 10 x 3 sparse Matrix of class "dgCMatrix"
## 1 2 3
## [1,] 5 . .
## [2,] . . 4
## [3,] 3 . .
## [4,] 3 . .
## [5,] . . 5
## [6,] . . 4
## [7,] 4 . .
## [8,] . . 4
## [9,] . . 4
## [10,] . . 3A more condensed look of the ratings.
vec_ratings <- as.vector(ratingsMatrix@data)
# Unique ratings
unique(vec_ratings)## [1] 5 0 3 4 2 1data.frame(ratings = getRatings(ratingsMatrix)) %>%
ggplot(aes(ratings)) + geom_bar(width = 0.75)
( ratingsMatrix <- ratingsMatrix[ , colCounts(ratingsMatrix) != 0] )## 2674 x 3 rating matrix of class 'realRatingMatrix' with 2704 ratings.table(rowCounts(ratingsMatrix))##
## 1 2 3
## 2650 18 6min(rowCounts(ratingsMatrix))## [1] 13.3 Divide up the data
net_evaluation <- ratingsMatrix %>% evaluationScheme(method = "split", train = 0.80, given = 1)
net_evaluation## Evaluation scheme with 1 items given
## Method: 'split' with 1 run(s).
## Training set proportion: 0.800
## Good ratings: NA
## Data set: 2674 x 3 rating matrix of class 'realRatingMatrix' with 2704 ratings.model_params <- list(method="cosine",
nn = 5,
sample = FALSE,
normalize = "center")train <- getData(net_evaluation, "train")
train## 2139 x 3 rating matrix of class 'realRatingMatrix' with 2164 ratings.test_known <- getData(net_evaluation, "known")
test_known## 535 x 3 rating matrix of class 'realRatingMatrix' with 248 ratings.test_unknown <- getData(net_evaluation, "unknown")
test_unknown## 535 x 3 rating matrix of class 'realRatingMatrix' with 292 ratings.3.3.1 Popular
model_method <- "POPULAR"
# Training
modelPOP <- Recommender(train, method = model_method)predPOP <- predict(modelPOP, test_known, type = "ratings")
# Accuracy
accPOP <- calcPredictionAccuracy(predPOP, test_unknown)accPOP## RMSE MSE MAE
## NaN NaN NaNWith sparse matrixes NaN can occur if there is not enough data.
3.3.2 Get the top N
With popular recommendation model we can also distinguish the top n items from the data set.
getModel(modelPOP)## $topN
## Recommendations as 'topNList' with n = 3 for 1 users.
##
## $ratings
## 1 x 3 rating matrix of class 'realRatingMatrix' with 3 ratings.
## Normalized using center on rows.
##
## $normalize
## [1] "center"
##
## $aggregationRatings
## standardGeneric for "colMeans" defined from package "base"
##
## function (x, na.rm = FALSE, dims = 1, ...)
## standardGeneric("colMeans")
## <bytecode: 0x0000000023632d38>
## <environment: 0x0000000022f3b0e0>
## Methods may be defined for arguments: x
## Use showMethods(colMeans) for currently available ones.
##
## $aggregationPopularity
## standardGeneric for "colSums" defined from package "base"
##
## function (x, na.rm = FALSE, dims = 1, ...)
## standardGeneric("colSums")
## <bytecode: 0x00000000236312e8>
## <environment: 0x0000000023100900>
## Methods may be defined for arguments: x
## Use showMethods(colSums) for currently available ones.
##
## $verbose
## [1] FALSEgetModel(modelPOP)$topN## Recommendations as 'topNList' with n = 3 for 1 users.The result contains two ordered top-N recommendation lists, one for each user. The recommended items can be inspected as a list.
recom <- predict(modelPOP, test_known, n=3)
head(as(recom,"list"))## [[1]]
## character(0)
##
## [[2]]
## [1] "1" "2"
##
## [[3]]
## [1] "1" "2"
##
## [[4]]
## [1] "1" "2"
##
## [[5]]
## [1] "3" "2"
##
## [[6]]
## [1] "1" "2"recom3 <- bestN(recom, n = 3)
head(as(recom3, "list"))## [[1]]
## character(0)
##
## [[2]]
## [1] "1" "2"
##
## [[3]]
## [1] "1" "2"
##
## [[4]]
## [1] "1" "2"
##
## [[5]]
## [1] "3" "2"
##
## [[6]]
## [1] "1" "2"3.3.3 Random
model_method <- "RANDOM"
# Training
modelRND <- Recommender(train, method = model_method)predRND <- predict(modelRND, test_known, type = "ratings")
# Accuracy
accRND <- calcPredictionAccuracy(predRND, test_unknown)accRND## RMSE MSE MAE
## NaN NaN NaN3.3.4 LIBMF
model_method <- "LIBMF"
# Training
modelLIBMF <- Recommender(train, method = model_method)predLIBMF <- predict(modelLIBMF, test_known, type = "ratings")## iter tr_rmse obj
## 0 2.6627 1.7589e+04
## 1 1.0069 2.9776e+03
## 2 0.5831 1.3386e+03
## 3 0.4644 1.0317e+03
## 4 0.3966 8.8933e+02
## 5 0.3489 8.0312e+02
## 6 0.3132 7.4920e+02
## 7 0.2852 7.0952e+02
## 8 0.2598 6.7308e+02
## 9 0.2407 6.5164e+02
## 10 0.2186 6.2198e+02
## 11 0.2055 6.0582e+02
## 12 0.1900 5.8776e+02
## 13 0.1751 5.7015e+02
## 14 0.1659 5.5947e+02
## 15 0.1580 5.5067e+02
## 16 0.1486 5.4139e+02
## 17 0.1387 5.2939e+02
## 18 0.1315 5.2111e+02
## 19 0.1262 5.1594e+02# Accuracy
accLIBMF <- calcPredictionAccuracy(predLIBMF, test_unknown)accLIBMF## RMSE MSE MAE
## 0.9963241 0.9926618 0.71882043.4 conclusion
LIBMF and popular was more accurate than the RANDOM method.
4 Book ratings
bookratings <- read.csv("https://raw.githubusercontent.com/moiyajosephs/Data-607-Projects/main/Final%20Project/Ratings.csv", header = TRUE, sep = ",", dec = ".")Check the class for each column since pare matrix and rea matrix are very partovulat
head(bookratings)## User.ID ISBN Book.Rating
## 1 276725 034545104X 0
## 2 276726 0155061224 5
## 3 276727 0446520802 0
## 4 276729 052165615X 3
## 5 276729 0521795028 6
## 6 276733 2080674722 0class(bookratings$User.ID); class(bookratings$ISBN); class(bookratings$Book.Rating);## [1] "integer"## [1] "character"## [1] "integer"The dataset is very large so i will take only 10000 ratings.
booksratings <- sample_n(bookratings, 10000)had to change this to factors
bookratings$ISBN <- as.factor(bookratings$ISBN)Convert into ratings matrix
ratingsMatrix <- sparseMatrix(as.integer(bookratings$User.ID), as.integer(bookratings$ISBN), x = bookratings$Book.Rating)
colnames(ratingsMatrix) <- levels(bookratings$ISBN)
rownames(ratingsMatrix) <- levels(bookratings$User.ID)
books <- as(ratingsMatrix, "realRatingMatrix")getRatingMatrix(books)[1:10,1:4]## 10 x 4 sparse Matrix of class "dgCMatrix"
## '9607092856' '9607092910' 0330299891 0375404120
## [1,] . . . .
## [2,] . . . .
## [3,] . . . .
## [4,] . . . .
## [5,] . . . .
## [6,] . . . .
## [7,] . . . .
## [8,] . . . .
## [9,] . . . .
## [10,] . . . .data.frame(ratings = getRatings(books)) %>%
ggplot(aes(ratings)) + geom_bar(width = 0.75)
(rowCounts(books))## [1] 0 1 0 0 0 0 1 18 3 2 0 1
## [13] 0 4 0 2 7 0 1 1 0 4 1 0
## [25] 0 2 0 0 0 0 0 3 0 0 0 1
## [37] 0 2 3 0 0 1 0 5 0 0 0 0
## [49] 0 0 1 0 7 0 0 3 0 0 0 0
## [61] 0 0 0 1 0 0 4 1 6 1 0 0
## [73] 1 0 4 0 1 1 1 0 1 1 1 0
## [85] 2 1 3 1 0 0 1 3 0 0 1 0
## [97] 1 0 12 1 0 1 0 0 0 0 1 0
## [109] 1 1 0 0 0 8 0 0 0 0 0 0
## [121] 0 0 0 0 1 0 0 0 1 0 0 1
## [133] 3 0 2 0 1 0 3 0 1 0 0 1
## [145] 0 0 0 0 0 0 1 0 0 0 0 0
## [157] 0 0 0 10 0 1 0 0 5 0 0 0
## [169] 1 0 0 0 0 0 0 5 0 3 0 0
## [181] 0 7 136 0 2 0 0 0 0 1 0 0
## [193] 1 0 0 0 0 0 5 0 0 1 0 2
## [205] 0 0 1 0 0 0 0 1 0 0 1 0
## [217] 1 0 0 0 1 0 0 1 0 1 0 2
## [229] 0 1 0 6 2 0 0 1 2 0 0 0
## [241] 1 41 81 8 0 0 2 0 1 1 0 0
## [253] 0 314 0 4 2 0 0 0 0 0 0 0
## [265] 0 0 0 0 1 0 0 7 1 0 0 0
## [277] 0 1 0 1 0 0 0 0 0 0 0 0
## [289] 2 0 1 0 0 0 0 0 0 0 0 9
## [301] 0 1 0 0 0 1 0 0 0 1 1 0
## [313] 1 0 0 1 0 0 0 0 0 0 1 0
## [325] 0 1 1 0 0 0 0 1 0 2 0 0
## [337] 1 1 1 0 0 0 1 0 1 0 0 0
## [349] 0 0 0 0 0 0 0 1 1 0 1 0
## [361] 3 8 0 1 0 0 1 0 1 0 0 1
## [373] 0 0 0 2 0 0 0 0 0 2 34 10
## [385] 0 0 8 24 0 0 0 66 3 0 0 0
## [397] 0 0 0 1 0 0 0 0 0 2 0 21
## [409] 0 0 0 0 4 0 0 0 0 0 0 0
## [421] 0 1 0 13 0 0 0 0 0 1 1 4
## [433] 4 0 1 0 0 1 1 4 3 0 0 7
## [445] 0 28 1 0 0 0 0 0 1 0 0 0
## [457] 0 0 0 7 0 0 1 0 0 0 0 0
## [469] 1 0 0 8 0 0 1 8 0 0 1 0
## [481] 0 0 1 0 0 6 27 0 1 0 0 0
## [493] 1 1 0 3 0 0 1 3 2 0 29 0
## [505] 16 0 131 2 0 0 0 1 1 0 0 1
## [517] 0 2 0 0 0 0 1 0 0 0 0 0
## [529] 0 1 0 0 0 0 0 0 1 2 0 0
## [541] 0 1 0 0 0 1 1 0 0 0 0 1
## [553] 0 0 0 0 0 1 2 0 0 0 0 1
## [565] 2 11 10 0 1 0 0 0 0 0 0 0
## [577] 0 0 0 0 0 0 0 0 1 0 0 0
## [589] 0 0 0 1 0 0 8 3 0 0 0 0
## [601] 0 1 0 0 0 0 0 0 0 0 0 0
## [613] 0 1 0 0 0 2 8 4 1 0 1 0
## [625] 15 54 0 2 0 0 0 0 0 1 0 0
## [637] 0 73 1 4 1 0 91 0 1 0 0 0
## [649] 1 1 42 0 0 0 0 0 3 0 0 1
## [661] 0 0 0 0 0 0 0 0 0 0 0 0
## [673] 0 0 0 0 1 2 0 0 0 0 1 0
## [685] 2 0 0 0 0 0 0 0 0 0 4 0
## [697] 0 2 0 0 1 0 0 0 4 0 3 1
## [709] 29 0 4 7 1 6 0 0 2 0 0 1
## [721] 0 0 0 1 0 4 0 20 0 3 0 0
## [733] 0 0 20 1 1 0 1 0 69 0 2 3
## [745] 0 7 0 0 0 0 0 0 16 0 0 1
## [757] 1 4 1 0 0 0 4 0 1 0 2 0
## [769] 1 0 1 0 0 1 0 7 5 1 0 1
## [781] 0 1 0 0 1 2 0 0 0 4 1 0
## [793] 0 0 1 0 0 0 1 0 0 0 0 0
## [805] 46 0 0 0 2 0 2 0 0 0 0 0
## [817] 0 1 7 0 0 1 0 1 0 0 8 1
## [829] 0 0 0 0 0 10 3 0 0 0 0 0
## [841] 0 0 10 0 0 0 0 1 1 42 0 0
## [853] 7 0 0 0 0 0 0 0 1 0 0 0
## [865] 0 0 0 0 0 0 0 0 0 0 0 18
## [877] 0 3 0 0 0 126 1 0 3 1 1 0
## [889] 0 0 0 0 0 0 1 2 0 0 12 13
## [901] 35 1 1 0 0 0 0 0 0 0 0 0
## [913] 3 26 5 1 0 0 1 0 1 0 0 1
## [925] 0 0 0 0 59 0 0 1 1 0 0 2
## [937] 1 2 0 0 1 0 0 1 18 0 0 0
## [949] 0 0 1 0 0 0 2 0 0 0 0 0
## [961] 0 0 0 0 1 0 1 0 1 2 0 0
## [973] 1 1 0 0 0 0 0 0 3 0 0 3
## [985] 0 1 0 0 8 0 0 0 0 0 0 4
## [997] 0 2 1 1 0 0 1 0 0 0 1 1
## [1009] 3 1 1 1 0 0 0 1 1 0 2 0
## [1021] 3 2 0 2 76 1 0 0 0 0 2 3
## [1033] 0 0 0 0 0 1 0 1 0 0 1 0
## [1045] 0 1 0 3 0 0 0 0 0 2 0 1
## [1057] 3 0 0 1 0 0 11 1 0 0 0 0
## [1069] 1 0 0 0 0 0 11 0 0 0 1 0
## [1081] 0 1 6 1 0 0 5 0 1 0 0 0
## [1093] 1 1 3 2 0 2 0 1 0 0 0 0
## [1105] 0 1 0 1 0 0 2 0 10 0 0 10
## [1117] 0 2 0 0 0 0 3 1 0 0 0 0
## [1129] 13 0 46 1 6 0 0 0 1 1 0 2
## [1141] 0 0 0 0 0 0 0 0 0 1 0 0
## [1153] 0 0 19 0 0 0 0 0 7 0 0 1
## [1165] 0 1 18 1 4 1 0 2 0 1 0 0
## [1177] 0 6 0 1 0 0 0 17 0 0 0 0
## [1189] 1 0 0 8 2 0 0 0 0 0 0 1
## [1201] 0 0 0 0 0 3 0 0 0 1 79 0
## [1213] 0 2 0 0 0 0 3 0 0 0 1 0
## [1225] 1 0 0 0 0 0 5 0 1 1 3 0
## [1237] 3 1 0 0 0 1 0 0 0 0 0 23
## [1249] 5 1 0 1 0 9 0 1 0 2 1 2
## [1261] 5 2 1 2 0 1 1 0 0 1 0 0
## [1273] 0 0 1 0 0 0 3 0 1 0 0 0
## [1285] 0 0 0 0 0 0 0 0 1 4 0 1
## [1297] 11 0 0 1 0 0 2 0 0 0 0 0
## [1309] 4 0 0 0 0 2 0 0 1 0 0 0
## [1321] 0 0 0 1 1 1 0 0 0 1 5 3
## [1333] 0 2 0 0 0 0 0 0 0 0 15 1
## [1345] 1 0 0 5 0 0 0 0 0 0 1 0
## [1357] 0 0 1 1 2 0 0 0 0 0 0 5
## [1369] 1 0 0 13 0 0 1 4 1 2 0 0
## [1381] 0 0 0 0 0 0 0 0 1 1 0 0
## [1393] 0 0 2 1 0 1 4 0 0 0 1 0
## [1405] 0 0 0 0 6 1 0 9 0 0 0 1
## [1417] 1 0 0 0 7 0 0 137 0 0 1 0
## [1429] 0 0 0 0 1 1 119 12 0 0 1 0
## [1441] 0 1 0 0 0 0 0 1 0 0 0 0
## [1453] 0 0 1 0 0 0 0 0 1 0 0 0
## [1465] 0 5 6 0 1 0 0 0 1 0 1 0
## [1477] 1 0 0 0 0 0 0 1 21 1 1 0
## [1489] 1 1 1 0 0 0 0 1 0 0 3 1
## [1501] 0 1 0 1 4 1 0 0 0 0 2 0
## [1513] 1 0 1 0 5 0 1 0 1 0 0 0
## [1525] 0 0 0 0 0 1 0 0 2 0 2 0
## [1537] 0 0 1 0 1 0 1 0 1 4 2 23
## [1549] 0 1 0 1 0 2 1 1 2 6 1 0
## [1561] 0 0 0 4 0 0 2 0 1 2 0 0
## [1573] 1 0 0 0 0 1 0 0 0 2 1 0
## [1585] 16 1 0 1 1 0 1 0 0 0 0 7
## [1597] 4 0 1 0 0 0 0 0 0 0 0 13
## [1609] 0 1 0 0 0 0 1 1 2 1 4 0
## [1621] 0 0 0 0 0 0 0 0 0 0 0 0
## [1633] 1 0 0 0 0 0 1 0 1 0 1 0
## [1645] 0 1 0 0 0 1 0 7 1 1 0 0
## [1657] 0 0 0 10 4 0 0 1 0 0 11 0
## [1669] 0 0 0 1 1 57 0 1 0 0 0 0
## [1681] 0 0 1 1 0 0 2 10 0 0 0 0
## [1693] 0 0 0 3 0 0 0 0 0 0 1 0
## [1705] 3 8 0 0 0 0 0 0 0 0 0 0
## [1717] 0 1 4 0 1 0 2 0 17 0 0 0
## [1729] 3 2 0 0 156 0 0 0 0 0 0 0
## [1741] 0 0 0 0 10 0 0 0 0 4 0 0
## [1753] 0 0 0 0 0 0 2 0 1 1 0 0
## [1765] 1 0 0 1 0 1 0 0 1 0 1 0
## [1777] 0 0 0 0 1 2 0 0 1 0 0 0
## [1789] 0 6 3 2 1 0 0 0 14 0 3 0
## [1801] 0 1 1 0 9 0 3 0 1 0 0 20
## [1813] 1 0 0 0 0 0 0 0 0 0 0 0
## [1825] 0 0 1 0 1 3 0 0 0 0 1 0
## [1837] 0 6 0 0 1 0 0 1 0 1 0 61
## [1849] 0 12 0 0 0 0 0 0 0 0 2 0
## [1861] 1 0 4 0 0 0 1 0 3 0 0 0
## [1873] 0 0 0 0 0 1 0 0 2 0 0 0
## [1885] 0 0 0 1 0 0 3 1 0 0 0 0
## [1897] 0 4 0 3 4 0 169 1 1 0 0 0
## [1909] 0 1 0 0 0 0 1 0 0 1 0 0
## [1921] 1 2 5 1 0 0 1 28 0 1 0 0
## [1933] 1 0 0 0 1 1 0 0 0 0 0 0
## [1945] 1 0 1 1 2 0 1 0 0 1 0 2
## [1957] 0 0 0 1 2 0 1 0 1 1 0 0
## [1969] 1 0 1 0 3 0 0 0 0 1 0 0
## [1981] 0 0 1 1 0 1 1 0 1 13 0 0
## [1993] 1 0 1 3 0 0 0 0 0 0 0 0
## [2005] 0 0 0 0 10 26 0 23 1 0 0 0
## [2017] 0 1 2 1 0 0 0 26 0 0 0 0
## [2029] 0 53 0 0 198 0 1 2 0 1 0 0
## [2041] 13 0 2 0 1 21 0 0 0 0 1 0
## [2053] 0 0 0 0 0 0 0 1 1 0 1 0
## [2065] 0 2 0 0 0 1 0 0 0 4 1 2
## [2077] 1 1 0 0 0 1 0 18 0 0 2 0
## [2089] 1 23 0 0 0 0 0 0 1 0 1 0
## [2101] 0 0 18 1 1 6 0 1 1 163 0 0
## [2113] 0 0 4 1 0 0 1 0 0 0 0 0
## [2125] 0 0 1 0 2 1 0 55 0 11 3 10
## [2137] 0 1 7 0 1 0 0 0 0 0 1 1
## [2149] 0 1 0 4 0 0 1 0 0 1 0 2
## [2161] 0 0 3 0 0 0 0 0 1 2 0 4
## [2173] 0 0 0 0 0 1 53 0 1 0 2 0
## [2185] 0 0 0 0 8 5 0 0 0 0 0 1
## [2197] 12 1 1 0 0 0 2 1 0 0 0 0
## [2209] 7 0 0 0 0 0 0 1 0 1 1 0
## [2221] 0 2 1 1 1 1 0 0 0 1 0 1
## [2233] 0 0 0 0 0 3 0 2 0 0 0 1
## [2245] 0 0 0 0 0 1 0 0 2 0 5 0
## [2257] 0 0 0 0 0 0 0 0 0 1 0 1
## [2269] 0 1 1 0 0 0 1 498 0 0 0 0
## [2281] 6 0 1 0 0 0 11 40 0 2 0 1
## [2293] 0 0 5 2 0 0 0 0 0 1 0 0
## [2305] 1 0 0 1 0 0 1 1 37 0 0 1
## [2317] 0 0 0 0 0 0 0 4 0 16 0 0
## [2329] 1 1 0 0 10 1 1 1 18 0 1 1
## [2341] 0 0 0 0 1 0 0 0 9 1 1 2
## [2353] 0 4 1 0 1 128 1 0 0 0 25 0
## [2365] 0 1 0 1 1 0 0 0 0 1 8 1
## [2377] 0 0 1 0 0 0 0 0 3 1 0 1
## [2389] 7 0 0 0 0 0 1 0 0 1 10 0
## [2401] 0 0 1 3 0 14 0 0 0 0 11 0
## [2413] 0 3 2 1 1 0 0 0 2 0 0 0
## [2425] 1 0 0 0 0 0 0 0 0 0 0 1
## [2437] 13 0 10 0 0 132 0 0 0 0 0 1
## [2449] 0 0 2 0 1 0 2 0 1 0 1 0
## [2461] 2 7 0 0 0 3 0 0 0 5 0 0
## [2473] 1 0 0 0 0 0 0 1 3 1 0 1
## [2485] 1 0 0 6 0 0 0 0 0 0 0 0
## [2497] 0 0 1 1 1 0 1 1 0 2 0 3
## [2509] 0 0 0 0 0 0 4 0 1 0 0 2
## [2521] 0 0 0 1 1 0 0 1 2 0 3 0
## [2533] 0 2 0 0 0 0 0 1 0 1 0 1
## [2545] 5 1 0 0 9 1 0 4 0 0 2 0
## [2557] 0 0 6 0 0 2 0 0 1 1 1 1
## [2569] 1 0 0 0 0 1 0 1 0 2 1 2
## [2581] 0 2 1 1 0 0 0 1 13 1 2 1
## [2593] 0 0 1 7 3 2 3 0 0 0 0 0
## [2605] 1 0 0 1 0 0 1 0 1 0 2 1
## [2617] 1 1 0 0 0 6 0 0 0 0 0 0
## [2629] 0 14 0 2 1 1 0 0 0 2 0 1
## [2641] 0 0 1 2 0 0 0 0 0 0 3 0
## [2653] 2 0 6 1 0 2 0 0 2 0 0 0
## [2665] 5 0 0 1 0 1 0 0 0 0 0 0
## [2677] 0 3 1 0 1 0 1 0 19 0 1 5
## [2689] 0 1 0 0 4 0 0 1 0 0 0 1
## [2701] 0 0 0 0 1 0 0 1 0 0 1 1
## [2713] 0 0 0 0 0 1 23 0 0 0 1 1
## [2725] 0 0 0 2 1 0 6 0 0 1 0 0
## [2737] 0 14 0 0 0 1 0 1 0 1 0 0
## [2749] 2 0 0 0 0 0 0 0 0 0 2 10
## [2761] 0 0 0 0 1 274 0 1 13 5 2 0
## [2773] 0 0 0 3 0 0 0 0 1 1 0 1
## [2785] 0 0 0 0 7 15 1 0 45 0 0 0
## [2797] 0 4 15 0 0 0 7 1 0 0 1 1
## [2809] 0 0 0 0 0 0 0 0 0 0 0 0
## [2821] 0 0 0 0 0 0 0 0 0 1 0 0
## [2833] 0 0 0 0 1 0 0 0 0 0 0 1
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## [2857] 0 0 1 0 1 0 1 1 0 0 0 0
## [2869] 1 1 1 1 0 0 0 0 0 0 0 0
## [2881] 0 1 1 0 1 0 0 0 1 0 168 1
## [2893] 9 0 0 5 0 0 0 1 0 0 2 0
## [2905] 0 9 7 0 0 0 0 0 0 1 0 0
## [2917] 0 0 0 0 0 0 0 0 0 0 0 0
## [2929] 0 0 0 1 2 0 7 4 0 0 4 0
## [2941] 0 1 2 0 1 2 0 1 0 1 0 0
## [2953] 0 20 0 0 1 0 0 0 0 1 0 1
## [2965] 0 1 0 4 0 0 0 1 1 1 0 0
## [2977] 232 0 1 1 0 5 1 1 1 0 0 0
## [2989] 0 0 1 1 0 0 1 17 0 0 0 0
## [3001] 0 0 3 0 2 0 0 0 1 0 0 0
## [3013] 0 0 0 0 15 0 0 4 0 0 2 0
## [3025] 0 1 1 0 0 1 0 2 0 0 1 0
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## [3061] 1 1 2 0 0 12 1 0 0 0 3 0
## [3073] 5 1 0 0 1 0 5 0 1 0 0 0
## [3085] 0 0 1 7 3 0 0 0 2 1 1 2
## [3097] 0 0 4 8 2 0 0 5 0 6 0 0
## [3109] 0 0 4 0 3 1 0 0 0 0 0 0
## [3121] 0 0 0 0 0 0 0 0 0 0 0 0
## [3133] 1 0 0 0 0 0 0 0 1 6 0 0
## [3145] 67 1 0 1 0 1 0 0 0 7 0 0
## [3157] 0 0 0 0 0 0 0 0 3 1 76 6
## [3169] 0 0 0 1 0 1 1 0 3 0 0 0
## [3181] 0 0 0 0 1 3 0 0 4 0 3 9
## [3193] 2 1 0 0 2 0 0 1 0 1 0 0
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## [3229] 0 0 0 6 0 0 0 2 2 0 0 0
## [3241] 0 0 1 0 0 0 3 0 0 0 2 0
## [3253] 0 0 0 0 2 0 0 1 1 1 0 0
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## [3289] 4 0 1 0 2 0 0 1 0 0 0 1
## [3301] 0 1 0 0 3 0 7 0 0 2 0 0
## [3313] 0 0 2 0 0 0 0 1 3 0 1 0
## [3325] 0 57 0 1 13 0 19 9 0 0 0 0
## [3337] 0 0 0 0 0 0 2 5 0 86 2 0
## [3349] 0 6 1 0 0 0 7 0 2 0 0 0
## [3361] 0 3 901 5 0 0 0 6 0 0 116 0
## [3373] 61 12 0 1 0 0 6 0 0 0 0 0
## [3385] 1 1 0 1 0 0 1 0 2 0 1 1
## [3397] 0 0 0 1 1 1 0 1 0 0 0 1
## [3409] 0 0 0 0 0 0 0 15 0 10 0 0
## [3421] 1 2 2 0 0 0 6 0 0 0 0 1
## [3433] 1 0 1 0 6 0 0 0 0 0 0 0
## [3445] 1 0 0 0 1 0 0 1 0 1 0 0
## [3457] 0 0 0 2 0 10 3 0 0 0 5 0
## [3469] 0 0 11 4 1 0 0 0 2 0 1 1
## [3481] 12 0 0 0 0 0 1 9 0 0 0 1
## [3493] 1 0 1 1 0 0 0 0 0 1 0 0
## [3505] 3 1 1 8 1 3 0 3 0 1 0 0
## [3517] 0 0 1 0 1 0 0 0 0 0 0 1
## [3529] 1 0 0 1 1 1 0 0 6 50 0 0
## [3541] 1 11 4 1 0 2 1 0 1 2 0 0
## [3553] 0 1 0 66 0 0 0 0 0 0 0 1
## [3565] 0 0 0 0 11 1 1 0 4 0 1 0
## [3577] 0 21 0 3 0 0 4 0 0 1 0 1
## [3589] 0 0 0 0 1 16 0 4 0 0 3 0
## [3601] 3 1 1 1 3 0 0 0 0 0 1 0
## [3613] 1 0 1 0 0 0 1 0 0 1 0 0
## [3625] 0 0 0 1 0 1 0 1 1 0 0 0
## [3637] 0 0 0 18 1 0 15 0 1 0 1 2
## [3649] 0 0 1 1 0 1 3 1 0 0 6 3
## [3661] 0 0 0 0 1 0 1 1 10 2 1 0
## [3673] 1 1 74 0 1 0 1 0 0 0 0 1
## [3685] 0 1 0 0 0 1 0 0 16 1 0 0
## [3697] 0 0 0 0 0 1 0 1 0 0 0 0
## [3709] 2 0 1 1 1 0 0 0 0 0 18 0
## [3721] 0 0 0 0 0 0 0 7 0 0 1 0
## [3733] 0 0 0 0 0 0 5 4 0 0 31 0
## [3745] 1 0 0 0 0 0 0 6 0 0 0 1
## [3757] 605 0 0 0 8 2 0 1 2 0 2 2
## [3769] 0 1 1 1 0 0 6 1 3 0 1 3
## [3781] 0 1 0 0 0 0 0 0 1 0 11 7
## [3793] 0 0 0 0 0 2 5 1 0 0 0 0
## [3805] 0 0 0 0 0 0 0 0 2 8 0 7
## [3817] 11 0 0 0 2 0 2 0 0 0 111 0
## [3829] 1 0 0 0 0 0 0 1 1 3 2 42
## [3841] 0 1 1 2 0 0 0 0 0 0 0 2
## [3853] 2 0 0 20 1 0 0 1 2 1 0 0
## [3865] 1 0 0 0 3 0 1 0 0 7 2 2
## [3877] 0 0 3 0 15 0 0 1 1 2 0 1
## [3889] 0 1 0 0 1 1 0 1 1 0 0 0
## [3901] 0 1 0 0 0 0 0 0 0 1 0 1
## [3913] 0 0 45 1 30 4 0 0 1 0 49 0
## [3925] 0 1 1 0 0 1 1 0 0 2 1 2
## [3937] 1 1 2 1 0 0 2 4 0 0 2 2
## [3949] 0 1 1 1 1 6 0 0 0 0 2 1
## [3961] 0 0 0 0 6 1 0 0 0 0 0 0
## [3973] 0 0 1 0 9 0 0 0 0 0 6 0
## [3985] 1 1 0 0 0 2 1 0 0 2 0 0
## [3997] 0 0 1 1 0 1 0 0 1 0 0 1
## [4009] 0 0 0 0 0 0 0 1 211 1 0 1
## [4021] 0 0 0 1 0 0 0 2 0 0 0 2
## [4033] 1 0 2 0 0 1 1 0 1 0 0 1
## [4045] 1 0 0 0 17 0 4 3 1 0 1 0
## [4057] 1 0 2 1 0 1 0 0 0 9 1 0
## [4069] 1 1 0 0 0 0 1 4 1 0 0 0
## [4081] 0 1 0 0 1 0 0 0 1 7 12 65
## [4093] 0 0 0 1 2 11 1 0 0 0 2 0
## [4105] 0 0 4 0 1 0 0 0 0 31 0 0
## [4117] 0 1 1 1 0 1 1 1 0 0 1 1
## [4129] 1 2 54 0 0 6 0 0 1 7 0 0
## [4141] 3 0 1 12 0 0 0 1 12 0 0 0
## [4153] 0 0 0 0 104 0 1 0 0 0 0 7
## [4165] 2 0 0 0 2 1 1 1 1 1 0 0
## [4177] 1 0 7 0 1 0 2 0 3 0 2 1
## [4189] 1 0 3 0 2 1 1 2 21 1 1 0
## [4201] 0 0 1 0 0 0 2 1 1 0 0 0
## [4213] 0 0 1 4 0 0 0 0 16 0 30 2
## [4225] 5 1 0 12 0 0 0 0 0 1 0 0
## [4237] 0 0 0 0 1 0 1 0 0 2 0 1
## [4249] 0 0 0 0 1 0 1 0 0 1 0 5
## [4261] 0 0 1 0 0 0 0 0 0 1 3 0
## [4273] 1 2 5 0 0 1 1 0 0 0 0 0
## [4285] 6 1 0 0 0 0 0 5 0 0 0 0
## [4297] 0 0 0 0 1 0 3 0 0 3 0 0
## [4309] 3 0 1 0 0 0 0 0 1 0 0 1
## [4321] 0 0 0 1 0 0 0 1 0 0 0 0
## [4333] 1 75 0 0 0 0 1 0 0 0 1 0
## [4345] 0 0 0 0 0 0 1 3 0 0 0 0
## [4357] 0 1 0 0 0 1 3 0 8 1 0 0
## [4369] 1 9 0 0 0 0 1 2 1 0 0 0
## [4381] 0 0 0 0 333 2 0 0 0 0 0 0
## [4393] 0 0 0 1 1 2 0 1 26 1 0 0
## [4405] 0 0 0 0 0 0 0 0 0 0 0 0
## [4417] 0 1 4 8 0 0 0 0 0 3 1 8
## [4429] 0 1 1 2 5 0 53 0 2 2 1 0
## [4441] 0 0 2 1 0 0 0 0 0 0 0 0
## [4453] 0 0 0 1 0 0 0 0 0 0 0 0
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## [4477] 0 0 2 0 0 0 8 0 0 0 0 0
## [4489] 1 0 0 0 0 0 1 0 1 0 0 0
## [4501] 2 0 0 1 0 2 0 1 1 0 0 1
## [4513] 0 30 1 2 2 17 0 0 1 0 0 1
## [4525] 1 0 0 1 0 0 0 0 0 0 2 0
## [4537] 1 0 1 1 5 0 0 1 4 0 10 1
## [4549] 0 0 1 0 0 0 0 4 0 1 0 0
## [4561] 1 0 0 0 0 0 0 0 0 11 11 0
## [4573] 1 1 0 0 1 0 4 0 0 1 0 1
## [4585] 0 0 0 0 0 4 3 0 0 0 0 0
## [4597] 0 1 1 21 0 1 0 0 0 0 0 1
## [4609] 0 0 0 0 0 5 0 0 0 1 1 0
## [4621] 0 67 0 0 0 0 0 0 0 0 0 0
## [4633] 0 0 0 0 0 0 1 1 0 0 0 0
## [4645] 0 0 0 0 0 0 0 0 1 0 0 0
## [4657] 0 0 1 0 0 0 0 0 1 0 1 0
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## [4681] 0 1 0 0 0 1 0 0 0 0 0 0
## [4693] 0 1 1 0 0 0 0 1 0 1 0 0
## [4705] 3 1 0 3 0 10 0 4 0 1 0 0
## [4717] 0 0 2 1 0 0 1 0 0 0 0 0
## [4729] 0 0 1 1 1 0 0 0 4 0 0 0
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## [99925] 0 1 4 0 1 0 0 2 2 0 0 2
## [99937] 0 1 1 0 0 2 1 0 0 21 0 0
## [99949] 0 0 2 0 1 11 200 0 0 2 0 0
## [99961] 1 0 7 0 0 1 0 1 1 1 0 0
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## [99985] 0 0 1 0 0 0 0 1 4 0 0 44
## [99997] 8 1 0
## [ reached getOption("max.print") -- omitted 178855 entries ]table(colCounts(books))[1:100]##
## 1 2 3 4 5 6 7 8 9 10 11
## 197045 59664 25976 14106 9008 6110 4477 3273 2578 2157 1745
## 12 13 14 15 16 17 18 19 20 21 22
## 1419 1207 1018 850 721 654 566 492 426 434 344
## 23 24 25 26 27 28 29 30 31 32 33
## 378 297 289 274 219 227 197 207 169 169 153
## 34 35 36 37 38 39 40 41 42 43 44
## 141 136 124 124 105 107 92 91 85 88 85
## 45 46 47 48 49 50 51 52 53 54 55
## 80 75 55 75 59 60 47 62 45 57 45
## 56 57 58 59 60 61 62 63 64 65 66
## 54 50 44 45 45 43 34 33 38 38 34
## 67 68 69 70 71 72 73 74 75 76 77
## 37 42 28 27 26 26 26 28 26 22 20
## 78 79 80 81 82 83 84 85 86 87 88
## 20 30 21 13 17 27 12 22 11 11 20
## 89 90 91 92 93 94 95 96 97 98 99
## 19 18 20 17 12 22 18 8 10 13 11
## 100
## 10( books <- books[ , colCounts(books) != 0] )## 278854 x 340556 rating matrix of class 'realRatingMatrix' with 1149780 ratings.books_subset <- books[ , colCounts(books) > 30]
books_subset <- books_subset[rowCounts(books_subset) > 10, ]
books_subset## 4546 x 4198 rating matrix of class 'realRatingMatrix' with 219188 ratings.getRatingMatrix(books_subset)[1:10,1:4]## 10 x 4 sparse Matrix of class "dgCMatrix"
## 000649840X 0006512135 0006547834 0006550681
## [1,] . . . .
## [2,] . . . .
## [3,] . . . .
## [4,] . . . .
## [5,] . . . .
## [6,] . . . .
## [7,] . . . .
## [8,] . . . .
## [9,] . . . .
## [10,] . . . .getRatings(books_subset)## [1] 8 0 0 8 0 0 5 3 0 8 8 0 0 0 0 9 0 10 0 0 0 6 8 7
## [25] 8 0 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 7 9 0 0
## [49] 0 0 0 0 0 5 0 0 0 0 0 0 0 0 5 0 10 9 0 0 0 0 0 0
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## [97] 0 0 0 0 7 8 0 5 10 9 0 0 8 0 0 8 0 0 0 0 0 0 7 0
## [121] 0 0 0 6 8 3 0 10 0 0 8 0 8 0 0 10 0 9 0 0 0 0 0 0
## [145] 9 0 0 0 0 0 8 0 0 9 0 0 0 7 0 0 0 0 0 0 0 5 7 8
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## [313] 9 0 9 0 0 7 0 0 0 10 0 0 0 0 0 0 0 0 8 10 8 10 0 0
## [337] 8 0 9 0 10 0 0 9 0 8 0 7 7 10 0 9 0 10 0 0 0 0 0 0
## [361] 0 0 8 8 0 0 0 10 0 0 0 0 10 9 0 0 0 0 5 0 0 8 0 9
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## [553] 0 8 0 0 0 0 8 8 0 0 10 0 0 8 0 5 0 6 0 7 0 5 0 7
## [577] 6 0 0 10 10 0 0 0 0 0 9 0 0 0 0 0 7 9 0 0 0 9 0 7
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## [769] 8 8 6 6 0 0 0 0 5 10 9 0 0 4 0 6 0 0 6 8 0 6 0 0
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## [1369] 0 0 5 0 8 0 6 0 7 0 0 0 9 0 8 8 10 0 7 10 0 9 0 0
## [1393] 8 0 8 0 8 10 0 0 8 0 0 8 0 0 5 7 6 8 0 0 0 7 0 8
## [1417] 9 0 0 9 0 9 0 0 10 0 0 7 0 0 0 0 10 0 0 0 0 0 10 9
## [1441] 0 0 0 7 8 0 9 0 0 0 8 7 9 0 0 0 0 8 0 0 0 7 0 0
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## [1657] 0 7 0 0 9 0 10 0 0 7 2 0 10 0 0 0 0 6 0 0 0 0 5 0
## [1681] 9 6 5 9 0 0 0 10 0 0 9 0 0 9 0 10 10 0 0 7 0 0 0 0
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## [1825] 0 0 0 9 0 9 5 5 6 0 0 0 0 0 0 0 0 8 0 0 0 8 0 8
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## [1897] 9 0 0 0 0 0 0 9 6 10 0 0 10 5 0 7 7 0 0 0 9 0 0 0
## [1921] 8 8 0 0 9 10 6 0 0 0 8 0 0 0 7 10 5 0 0 0 9 8 10 0
## [1945] 0 0 0 0 0 0 0 8 0 0 0 0 0 8 9 0 0 0 0 0 0 0 8 0
## [1969] 0 0 5 0 0 9 8 8 0 0 0 0 0 0 8 0 8 9 0 7 0 0 0 0
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## [ reached getOption("max.print") -- omitted 119189 entries ]data.frame(ratings = getRatings(books_subset)) %>%
ggplot(aes(ratings)) + geom_bar(width = 0.75)
4.1 Divide the data
Good rating is 5, the ratings go all the way up to 10
set.seed(88)
eval <- evaluationScheme(books_subset, method = "split", train = 0.8, given = 5, goodRating = 5)
train <- getData(eval, "train")
known <- getData(eval, "known")
unknown <- getData(eval, "unknown")4.1.1 Popular
model_method <- "POPULAR"
# Training
modelPOP <- Recommender(train, method = model_method)predPOP <- predict(modelPOP, newdata = known, type = "ratings")
# Accuracy
accPOP <- calcPredictionAccuracy(predPOP, unknown)accPOP## RMSE MSE MAE
## 3.648391 13.310758 2.798375The accuracy o the popular method did not perform as well on this dataset. Something with a user-based or item based may be better fit.
4.1.2 Finding the top book:
getModel(modelPOP)## $topN
## Recommendations as 'topNList' with n = 4198 for 1 users.
##
## $ratings
## 1 x 4198 rating matrix of class 'realRatingMatrix' with 4196 ratings.
## Normalized using center on rows.
##
## $normalize
## [1] "center"
##
## $aggregationRatings
## standardGeneric for "colMeans" defined from package "base"
##
## function (x, na.rm = FALSE, dims = 1, ...)
## standardGeneric("colMeans")
## <bytecode: 0x0000000023632d38>
## <environment: 0x0000000022f3b0e0>
## Methods may be defined for arguments: x
## Use showMethods(colMeans) for currently available ones.
##
## $aggregationPopularity
## standardGeneric for "colSums" defined from package "base"
##
## function (x, na.rm = FALSE, dims = 1, ...)
## standardGeneric("colSums")
## <bytecode: 0x00000000236312e8>
## <environment: 0x0000000023100900>
## Methods may be defined for arguments: x
## Use showMethods(colSums) for currently available ones.
##
## $verbose
## [1] FALSEgetModel(modelPOP)$topN## Recommendations as 'topNList' with n = 4198 for 1 users.The result contains two ordered top-N recommendation lists, one for each user. The recommended items can be inspected as a list.
recom <- predict(modelPOP, known, n=3)
head(as(recom,"list"))## [[1]]
## [1] "059035342X" "0385504209" "0316666343"
##
## [[2]]
## [1] "059035342X" "0385504209" "0316666343"
##
## [[3]]
## [1] "059035342X" "0385504209" "0316666343"
##
## [[4]]
## [1] "059035342X" "0385504209" "0316666343"
##
## [[5]]
## [1] "059035342X" "0385504209" "0316666343"
##
## [[6]]
## [1] "059035342X" "0385504209" "0316666343"recom3 <- bestN(recom, n = 3)
head(as(recom3, "list"))## [[1]]
## [1] "059035342X" "0385504209" "0316666343"
##
## [[2]]
## [1] "059035342X" "0385504209" "0316666343"
##
## [[3]]
## [1] "059035342X" "0385504209" "0316666343"
##
## [[4]]
## [1] "059035342X" "0385504209" "0316666343"
##
## [[5]]
## [1] "059035342X" "0385504209" "0316666343"
##
## [[6]]
## [1] "059035342X" "0385504209" "0316666343"4.1.3 USBF
model_method <- "UBCF"
# Training
model_params <- list(method = "cosine",
nn = 10, # find each user's 10 most similar users.
sample = FALSE, # already did this.
normalize = "center")
modelUBCF <- getData(eval, "train") %>% Recommender( method = model_method, parameter= model_params)predUBCF <- predict(modelUBCF, newdata = known, type = "ratings")Using USBF this method takes the longest time to run.
4.1.4 Accuracy
accUBCF <- calcPredictionAccuracy(predUBCF, unknown)accUBCF## RMSE MSE MAE
## 4.448668 19.790646 3.249633As shown above UBCF isn’t accurate in predicting future items. Possibly with more work it will work.
4.2 Conclusions
This was interesting to use to see which items would be the topN. Future iterations of the project could search for the item by the idea. This could be something useful for a business.
5 Conclusions
Popular methods seems to be the better choice based on this small look into recommender lab. However it is important to take into account which data set you are using. Something like User Based will work well with a lot of sampled users. Item based will work well with a lot of sampled
In conclusion, I was able to learn a lot about recommenderlab and what it would take to build a recommendation engine. Ways to improve in the future is to possible use a technology like Spark which can handle larger amounts of data. That way I could make some more accurate predictions on the dataset. Another possible improvement would be to use another recommendation algorithm library and see if it could potentially do a better job.