Project 4
The dataset contains product ratings for beauty products sold on Amazon.
• Original set contains 2,023,070 observations and 4 variables. It covers 1,210,271 users and 249,274 products. We will work with a subset of the data, otherwise running the project takes up long time.
ratings <- read.csv("Beauty.csv")Lets look at the first few records and ratings distribution
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 1404518400hist(ratings$Rating)
Step-3) Convert to realRatingMatrix
ratingsMatrix <- sparseMatrix(as.integer(ratings$UserId), as.integer(ratings$ProductId),
x = ratings$Rating)
colnames(ratingsMatrix) <- levels(ratings$ProductId)
rownames(ratingsMatrix) <- levels(ratings$UserId)
amazon <- as(ratingsMatrix, "realRatingMatrix")Step-4) Explore
amazon## 1210271 x 249274 rating matrix of class 'realRatingMatrix' with 2023070 ratings.hist(rowCounts(amazon))
table(rowCounts(amazon))##
## 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 1hist(colCounts(amazon))
table(colCounts(amazon))##
## 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 1Step-5) Subsetting
(amazonShort <- amazon[rowCounts(amazon) > 10, colCounts(amazon) > 30])## 10320 x 12057 rating matrix of class 'realRatingMatrix' with 111871 ratings.amazonShort <- amazon[, colCounts(amazon) > 30]
amazonShort <- amazonShort[rowCounts(amazonShort) > 10, ]
amazonShort## 3562 x 12057 rating matrix of class 'realRatingMatrix' with 68565 ratings.Step-6) Remove Empty Lines
table(rowCounts(amazonShort))##
## 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30
## 598 479 335 274 264 184 163 135 118 99 84 73 73 66 50 54 26 34 36 31
## 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50
## 31 30 22 18 17 15 11 20 15 13 11 8 7 9 8 5 10 10 4 5
## 51 52 53 54 55 56 57 58 60 61 62 63 64 65 66 67 68 69 70 71
## 8 4 7 6 4 2 3 3 1 2 4 4 1 2 4 4 2 2 1 1
## 72 73 74 75 76 77 78 79 81 85 86 90 91 93 94 95 97 98 99 100
## 1 3 2 5 2 5 1 3 3 2 1 1 2 1 1 1 1 1 1 1
## 101 103 105 110 111 112 114 116 119 126 152 185 187
## 1 1 2 1 1 1 1 1 1 1 1 1 1table(colCounts(amazonShort))##
## 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
## 2410 2404 1849 1231 918 587 451 323 217 192 147 114 96 77 51 60
## 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31
## 48 48 49 30 36 39 24 18 26 20 20 17 23 18 19 20
## 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47
## 21 15 16 10 14 16 10 14 8 12 10 8 9 15 11 9
## 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63
## 9 15 13 5 4 6 10 4 9 13 11 5 6 12 6 6
## 64 65 66 68 69 70 71 72 73 74 75 76 77 78 79 80
## 5 8 4 7 2 6 2 6 5 3 8 4 4 2 4 6
## 81 82 83 84 85 86 87 88 89 90 91 92 93 95 96 98
## 4 7 6 5 2 4 1 4 1 1 4 1 2 1 1 1
## 99 102 103 105 106 107 112 116 122 136 141 143 146 147 148 160
## 1 1 1 2 2 1 1 1 1 1 1 1 1 1 1 1
## 164 167 174 177
## 1 1 1 1(amazonShort <- amazonShort[, colCounts(amazonShort) != 0])## 3562 x 9647 rating matrix of class 'realRatingMatrix' with 68565 ratings.Step-7) Convert to data frame
ratings <- as.data.frame(as.matrix(amazonShort@data))
ratings$UserId <- rownames(ratings)
ratings <- ratings %>% gather(key = ProductId, value = Rating, -UserId) %>% filter(Rating !=
0)Data import and Data Preparation
ratings <- read.csv("finalratings.csv")
ratingsMatrix <- sparseMatrix(as.integer(ratings$UserId), as.integer(ratings$ProductId),
x = ratings$Rating)
colnames(ratingsMatrix) <- levels(ratings$ProductId)
rownames(ratingsMatrix) <- levels(ratings$UserId)
amazon <- as(ratingsMatrix, "realRatingMatrix")Train and Test sets
set.seed(1)
eval <- evaluationScheme(amazon, method = "split", train = 0.8, given = 5, goodRating = 3)
train <- getData(eval, "train")
known <- getData(eval, "known")
unknown <- getData(eval, "unknown")
timing <- data.frame(Model = factor(), Training = double(), Predicting = double())Models
USER BASED COLLABORATIVE FILTERING
model_method <- "UBCF"
# Train
tic()
modelUBCF <- Recommender(train, method = model_method)
t <- toc(quiet = TRUE)
train_time <- round(t$toc - t$tic, 2)
# Predict
tic()
predUBCF <- predict(modelUBCF, newdata = known, type = "ratings")
t <- toc(quiet = TRUE)
predict_time <- round(t$toc - t$tic, 2)
timing <- rbind(timing, data.frame(Model = as.factor(model_method), Training = as.double(train_time),
Predicting = as.double(predict_time)))
# Accuracy
accuracyUBCF <- calcPredictionAccuracy(predUBCF, unknown)RANDOM
model_method <- "RANDOM"
# Training
tic()
modelRandom <- Recommender(train, method = model_method)
t <- toc(quiet = TRUE)
train_time <- round(t$toc - t$tic, 2)
# Predicting
tic()
predRandom <- predict(modelRandom, newdata = known, type = "ratings")
t <- toc(quiet = TRUE)
predict_time <- round(t$toc - t$tic, 2)
timing <- rbind(timing, data.frame(Model = as.factor(model_method), Training = as.double(train_time),
Predicting = as.double(predict_time)))
# Accuracy
accuracyRandom <- calcPredictionAccuracy(predRandom, unknown)SVD
model_method <- "SVD"
# Train
tic()
modelSVD <- Recommender(train, method = model_method, parameter = list(k = 50))
t <- toc(quiet = TRUE)
train_time <- round(t$toc - t$tic, 2)
# Predict
tic()
predSVD <- predict(modelSVD, newdata = known, type = "ratings")
t <- toc(quiet = TRUE)
predict_time <- round(t$toc - t$tic, 2)
timing <- rbind(timing, data.frame(Model = as.factor(model_method), Training = as.double(train_time),
Predicting = as.double(predict_time)))
# Accuracy
accuracySVD <- calcPredictionAccuracy(predSVD, unknown)Compairing Models
Lets compare the accuracy of all three models
Based on the table below we see that UBCF and SVD have similar RMSE. As expected Random model have the least accuracy out of the three models.
accuracy <- rbind(accuracyUBCF, accuracyRandom)
accuracy <- rbind(accuracy, accuracySVD)
rownames(accuracy) <- c("UBCF", "Random", "SVD")
knitr::kable(accuracy, format = "html") %>% kableExtra::kable_styling(bootstrap_options = c("striped",
"hover"))| RMSE | MSE | MAE | |
|---|---|---|---|
| UBCF | 1.114454 | 1.242008 | 0.8056955 |
| Random | 1.389105 | 1.929612 | 0.9923223 |
| SVD | 1.113120 | 1.239035 | 0.7978551 |
ROC curve
Lets review ROC curve and Precision-Recall plot for all three models.
models <- list(UBCF = list(name = "UBCF", param = NULL), Random = list(name = "RANDOM",
param = NULL), SVD = list(name = "SVD", param = list(k = 50)))
evalResults <- evaluate(x = eval, method = models, n = c(1, 5, 10, 30, 60))## UBCF run fold/sample [model time/prediction time]
## 1 [0sec/147.61sec]
## RANDOM run fold/sample [model time/prediction time]
## 1 [0sec/4.65sec]
## SVD run fold/sample [model time/prediction time]
## 1 [10.65sec/5.04sec]plot(evalResults, annotate = TRUE, legend = "topleft", main = "ROC Curve")
# Precision-Recall Plot
plot(evalResults, "prec/rec", annotate = TRUE, legend = "topright", main = "Precision-Recall")
UBCF is better than SVD and alot better than Random model. As expected, Random model is the worse out of the models.
•Training and prediction time.
• From the table below we can see that the UBCF model can be trained quickly, but prediction takes significant amount of time. The Random model is very fast for training and predictions. The SVD model takes longer to train than to predict.
rownames(timing) <- timing$Model
knitr::kable(timing[, 2:3], format = "html") %>% kableExtra::kable_styling(bootstrap_options = c("striped",
"hover"))| Training | Predicting | |
|---|---|---|
| UBCF | 0.12 | 156.02 |
| RANDOM | 0.00 | 2.69 |
| SVD | 11.28 | 3.08 |
Implement Support for Business/User Experiance Goal
• Accuracy for UBCF and SVD models were similar and these are alot better than Random model. We will create a hybrid model consisting of UBCF and SVD models.
• Recommending products that are likely to be rated high by a user might not be desirable. Recommending unexpected products may improve user experience, expand user preferences, provide additional knowledge.
• To make sure that most of recommendations are still likely be highly rated, we only allow very minor influence of the Random model (0.99 vs. 0.01 weight between UBCF and Random models).
model_Hybrid <- HybridRecommender(modelUBCF, modelRandom, weights = c(0.99,
0.01))
pred_Hybrid <- predict(model_Hybrid, newdata = known, type = "ratings")
(accHybrid <- calcPredictionAccuracy(pred_Hybrid, unknown))## RMSE MSE MAE
## 1.3230367 1.7504262 0.9006966Compare Accuracy
Accuracy of the Hybrid model is less than SVD and UBCF, but it is better than random model. The goal here is to influence user experience rather than make the most accurate model, so we need to use different metrics.
Below is the prediction for first user in the test set. Hybrid model includes most of the items recommended by the UBCF model and also inlcudes new items and the order is different.
pUBCF <- predict(modelUBCF, newdata = known[1], type = "topNList")
pHybrid <- predict(model_Hybrid, newdata = known[1], type = "topNList")
pUBCF@items## $A03364251DGXSGA9PSR99
## [1] 6591 5924 6203 7215 1167 2760 5096 6705 5078 6729pHybrid@items## $A03364251DGXSGA9PSR99
## [1] 6203 7215 6591 2760 6705 2880 8827 1982 5096 1167We built three different recommender systems and compared the accuracy of those different models, similar process can be used to compare additional models or to adjust model parameters to find the most optimal model.
Additional experiments that worth exploring
• One of the approaches in measuring success of diversification is by using A/B testing. Users are randomly divided into two groups and each group is offered a slightly different experience. For instance, one group may get recommendations only from the UBCF model while the other group will get recommendations from the hybrid model.
• Track/explore other metrics - products bought, time spent on product page, amount spent, etc. We would like to explore if hybrid model provides meaningful improvement over basic model.
• Evaluation of a model that returns highly relevant, but redundant recommendations may score poorly in user experience.
• Measure diversity, as described in Novelty and Diversity in Information Retrieval Evaluation or similar measurements should be incorporated in projects of this type.