The dataset contains a sample of 5000 users from the anonymous ratings data from the Jester Online Joke Recommender System collected between April 1999 and May 2003 (Gold- berg, Roeder, Gupta, and Perkins 2001). The dataset contains ratings for 100 jokes on a scale from -10 to 10. All users in the data set have rated 36 or more jokes. Let's load the recommenderlab library and the Jester5K dataset:

> library("recommenderlab")
> data(Jester5k)
> Jester5k@data@Dimnames[2]
[[1]]
[1] "j1" "j2" "j3" "j4" "j5" "j6" "j7" "j8" "j9"
[10] "j10" "j11" "j12" "j13" "j14" "j15" "j16" "j17" "j18"
[19] "j19" "j20" "j21" "j22" "j23" "j24" "j25" "j26" "j27"
[28] "j28" "j29" "j30" "j31" "j32" "j33" "j34" "j35" "j36"
[37] "j37" "j38" "j39" "j40" "j41" "j42" "j43" "j44" "j45"
[46] "j46" "j47" "j48" "j49" "j50" "j51" "j52" "j53" "j54"
[55] "j55" "j56" "j57" "j58" "j59" "j60" "j61" "j62" "j63"
[64] "j64" "j65" "j66" "j67" "j68" "j69" "j70" "j71" "j72"
[73] "j73" "j74" "j75" "j76" "j77" "j78" "j79" "j80" "j81"
[82] "j82" "j83" "j84" "j85" "j86" "j87" "j88" "j89" "j90"
[91] "j91" "j92" "j93" "j94" "j95" "j96" "j97" "j98" "j99"
[100] "j100"

The following image shows the distribution of real ratings given by 2000 users:

> data<-sample(Jester5k,2000)

> hist(getRatings(data),breaks=100,col="blue")

The input dataset contains the individual ratings; normalization function reduces the individual rating bias by centering the row, which is a standard z-score transformation. Subtracting each element from the mean and then dividing by standard deviation. The following graph shows normalized ratings for the preceding dataset:

> hist(getRatings(normalize(data)),breaks=100,col="blue4")

To create a recommender system:

A recommendation engine is created using the recommender() function. A new recommendation algorithm can be added by the user using the recommenderRegistry$get_entries() function:

> recommenderRegistry$get_entries(dataType = "realRatingMatrix")
$IBCF_realRatingMatrix
Recommender method: IBCF
Description: Recommender based on item-based collaborative filtering (real data).
Parameters:
k method normalize normalize_sim_matrix alpha na_as_zero minRating
1 30 Cosine center FALSE 0.5 FALSE NA
$POPULAR_realRatingMatrix
Recommender method: POPULAR
Description: Recommender based on item popularity (real data).
Parameters: None
$RANDOM_realRatingMatrix
Recommender method: RANDOM
Description: Produce random recommendations (real ratings).
Parameters: None
$SVD_realRatingMatrix
Recommender method: SVD
Description: Recommender based on SVD approximation with column-mean imputation (real data).
Parameters:
k maxiter normalize minRating
1 10 100 center NA
$SVDF_realRatingMatrix
Recommender method: SVDF
Description: Recommender based on Funk SVD with gradient descend (real data).
Parameters:
k gamma lambda min_epochs max_epochs min_improvement normalize
1 10 0.015 0.001 50 200 1e-06 center
minRating verbose
1 NA FALSE
$UBCF_realRatingMatrix
Recommender method: UBCF
Description: Recommender based on user-based collaborative filtering (real data).
Parameters:
method nn sample normalize minRating
1 cosine 25 FALSE center NA

The preceding Registry command helps in identifying the methods available in recommenderlab, parameters for the model.

There are six different methods for implementing recommender system: popular, item-based, user based, PCA, random, and SVD. Let's start the recommendation engine using popular method:

> rc <- Recommender(Jester5k, method = "POPULAR")

> rc

Recommender of type 'POPULAR' for 'realRatingMatrix'

learned using 5000 users.

> names(getModel(rc))

[1] "topN" "ratings"

[3] "minRating" "normalize"

[5] "aggregationRatings" "aggregationPopularity"

[7] "minRating" "verbose"

> getModel(rc)$topN

Recommendations as 'topNList' with n = 100 for 1 users.

The objects such as top N, verbose, aggregation popularity, and others can be printed using names of the getmodel() command.

recom <- predict(rc, Jester5k, n=5)
recom

To generate recommendation, we can use the predict function against the same dataset and validate the accuracy of the predictive model. Here we are generating top 5 recommended jokes to each of the users. The result of the prediction is as follows:

> head(as(recom,"list"))

$u2841

[1] "j89" "j72" "j76" "j88" "j83"

$u15547

[1] "j89" "j93" "j76" "j88" "j91"

$u15221

character(0)

$u15573

character(0)

$u21505

[1] "j89" "j72" "j93" "j76" "j88"

$u15994

character(0)

For the same Jester5K dataset, let's try to implement item-based collaborative filtering (IBCF):

> rc <- Recommender(Jester5k, method = "IBCF")
> rc
Recommender of type 'IBCF' for 'realRatingMatrix'
learned using 5000 users.
> recom <- predict(rc, Jester5k, n=5)
> recom
Recommendations as 'topNList' with n = 5 for 5000 users.
> head(as(recom,"list"))
$u2841
[1] "j85" "j86" "j74" "j84" "j80"
$u15547
[1] "j91" "j87" "j88" "j89" "j93"
$u15221
character(0)
$u15573
character(0)
$u21505
[1] "j78" "j80" "j73" "j77" "j92"
$u15994
character(0)

Principal component analysis (PCA) method is not applicable for real rating-based dataset because getting correlation matrix and subsequent eigen vector and eigen value calculation would not be accurate. Hence, we will not show its application. Next we are going to show how the random method works:

> rc <- Recommender(Jester5k, method = "RANDOM")
> rc
Recommender of type 'RANDOM' for 'ratingMatrix'
learned using 5000 users.
> recom <- predict(rc, Jester5k, n=5)
> recom
Recommendations as 'topNList' with n = 5 for 5000 users.
> head(as(recom,"list"))
[[1]]
[1] "j90" "j74" "j86" "j78" "j85"
[[2]]
[1] "j87" "j88" "j74" "j92" "j79"
[[3]]
character(0)
[[4]]
character(0)
[[5]]
[1] "j95" "j86" "j93" "j78" "j83"
[[6]]
character(0)

In recommendation engine, the SVD approach is used to predict the missing ratings so that recommendation can be generated. Using singular value decomposition (SVD) method, the following recommendation can be generated:

> rc <- Recommender(Jester5k, method = "SVD")
> rc
Recommender of type 'SVD' for 'realRatingMatrix'
learned using 5000 users.
> recom <- predict(rc, Jester5k, n=5)
> recom
Recommendations as 'topNList' with n = 5 for 5000 users.
> head(as(recom,"list"))
$u2841
[1] "j74" "j71" "j84" "j79" "j80"
$u15547
[1] "j89" "j93" "j76" "j81" "j88"
$u15221
character(0)
$u15573
character(0)
$u21505
[1] "j80" "j73" "j100" "j72" "j78"
$u15994
character(0)

The result from the user-based collaborative filtering is shown next:

> rc <- Recommender(Jester5k, method = "UBCF")
> rc
Recommender of type 'UBCF' for 'realRatingMatrix'
learned using 5000 users.
> recom <- predict(rc, Jester5k, n=5)
> recom
Recommendations as 'topNList' with n = 5 for 5000 users.
> head(as(recom,"list"))
$u2841
[1] "j81" "j78" "j83" "j80" "j73"
$u15547
[1] "j96" "j87" "j89" "j76" "j93"
$u15221
character(0)
$u15573
character(0)
$u21505
[1] "j100" "j81" "j83" "j92" "j96"
$u15994
character(0)

Now let's compare the results obtained from all the five different algorithms, except PCA, because PCA requires a binary dataset and does not accept real ratings matrix.

One thing clear from the table is that for users 15573 and 15221, none of the five methods generate a recommendation. Hence, it is important to look at methods to evaluate the recommendation results. To validate the accuracy of the model let's implement accuracy measures and compare the accuracy of all the models.

For the evaluation of the model results, the dataset is divided into 90 percent for training and 10 percent for testing the algorithm. The definition of good rating is updated as 5:

> e <- evaluationScheme(Jester5k, method="split",
+ train=0.9,given=15, goodRating=5)
> e
Evaluation scheme with 15 items given
Method: 'split' with 1 run(s).
Training set proportion: 0.900
Good ratings: >=5.000000
Data set: 5000 x 100 rating matrix of class 'realRatingMatrix' with 362106 ratings.

The following script is used to build the collaborative filtering model, apply it on a new dataset for predicting the ratings and then the prediction accuracy is computed the error matrix is shown as follows:

> #User based collaborative filtering
> r1 <- Recommender(getData(e, "train"), "UBCF")
> #Item based collaborative filtering
> r2 <- Recommender(getData(e, "train"), "IBCF")
> #PCA based collaborative filtering
> #r3 <- Recommender(getData(e, "train"), "PCA")
> #POPULAR based collaborative filtering
> r4 <- Recommender(getData(e, "train"), "POPULAR")
> #RANDOM based collaborative filtering
> r5 <- Recommender(getData(e, "train"), "RANDOM")
> #SVD based collaborative filtering
> r6 <- Recommender(getData(e, "train"), "SVD")
> #Predicted Ratings
> p1 <- predict(r1, getData(e, "known"), type="ratings")
> p2 <- predict(r2, getData(e, "known"), type="ratings")
> #p3 <- predict(r3, getData(e, "known"), type="ratings")
> p4 <- predict(r4, getData(e, "known"), type="ratings")
> p5 <- predict(r5, getData(e, "known"), type="ratings")
> p6 <- predict(r6, getData(e, "known"), type="ratings")
> #calculate the error between the prediction and
> #the unknown part of the test data
> error <- rbind(
+ calcPredictionAccuracy(p1, getData(e, "unknown")),
+ calcPredictionAccuracy(p2, getData(e, "unknown")),
+ #calcPredictionAccuracy(p3, getData(e, "unknown")),
+ calcPredictionAccuracy(p4, getData(e, "unknown")),
+ calcPredictionAccuracy(p5, getData(e, "unknown")),
+ calcPredictionAccuracy(p6, getData(e, "unknown"))
+ )
> rownames(error) <- c("UBCF","IBCF","POPULAR","RANDOM","SVD")
> error
RMSE MSE MAE
UBCF 4.485571 20.12034 3.511709
IBCF 4.606355 21.21851 3.466738
POPULAR 4.509973 20.33985 3.548478
RANDOM 7.917373 62.68480 6.464369
SVD 4.653111 21.65144 3.679550

From the preceding result, UBCF has the lowest error in comparison to the other recommendation methods. Here, to evaluate the results of the predictive model, we are using k-fold cross validation method; k is assumed to be taken as 4:

> #Evaluation of a top-N recommender algorithm

> scheme <- evaluationScheme(Jester5k, method="cross", k=4,

+ given=3,goodRating=5)

> scheme

Evaluation scheme with 3 items given

Method: 'cross-validation' with 4 run(s).

Good ratings: >=5.000000

Data set: 5000 x 100 rating matrix of class 'realRatingMatrix' with 362106 ratings.

The results of the models from the evaluation scheme show the runtime versus the prediction time by different cross validation results for different models, the result is shown as follows:

> results <- evaluate(scheme, method="POPULAR", n=c(1,3,5,10,15,20))
POPULAR run fold/sample [model time/prediction time]
1 [0.14sec/2.27sec]
2 [0.16sec/2.2sec]
3 [0.14sec/2.24sec]
4 [0.14sec/2.23sec]
> results <- evaluate(scheme, method="IBCF", n=c(1,3,5,10,15,20))
IBCF run fold/sample [model time/prediction time]
1 [0.4sec/0.38sec]
2 [0.41sec/0.37sec]
3 [0.42sec/0.38sec]
4 [0.43sec/0.37sec]
> results <- evaluate(scheme, method="UBCF", n=c(1,3,5,10,15,20))
UBCF run fold/sample [model time/prediction time]
1 [0.13sec/6.31sec]
2 [0.14sec/6.47sec]
3 [0.15sec/6.21sec]
4 [0.13sec/6.18sec]
> results <- evaluate(scheme, method="RANDOM", n=c(1,3,5,10,15,20))
RANDOM run fold/sample [model time/prediction time]
1 [0sec/0.27sec]
2 [0sec/0.26sec]
3 [0sec/0.27sec]
4 [0sec/0.26sec]
> results <- evaluate(scheme, method="SVD", n=c(1,3,5,10,15,20))
SVD run fold/sample [model time/prediction time]
1 [0.36sec/0.36sec]
2 [0.35sec/0.36sec]
3 [0.33sec/0.36sec]
4 [0.36sec/0.36sec]

The confusion matrix displays the level of accuracy provided by each of the models, we can estimate the accuracy measures such as precision, recall and TPR, FPR, and so on the result is shown as follows:

> getConfusionMatrix(results)[[1]]

TP FP FN TN precision recall TPR FPR

1 0.2736 0.7264 17.2968 78.7032 0.2736000 0.01656597 0.01656597 0.008934588

3 0.8144 2.1856 16.7560 77.2440 0.2714667 0.05212659 0.05212659 0.027200530

5 1.3120 3.6880 16.2584 75.7416 0.2624000 0.08516269 0.08516269 0.046201487

10 2.6056 7.3944 14.9648 72.0352 0.2605600 0.16691259 0.16691259 0.092274243

15 3.7768 11.2232 13.7936 68.2064 0.2517867 0.24036802 0.24036802 0.139945095

20 4.8136 15.1864 12.7568 64.2432 0.2406800 0.30082509 0.30082509 0.189489883

Association rules as a method for recommendation engine for building product recommendation in a retail/e-commerce scenario, is used in Chapter 4, Regression with Automobile Data.