Binary Recommendation Engines in R

In this post, we will look at recommendation engines using binary information. For a binary recommendation engine, it requires that the data rates the product as good/bad or some other system in which only two responses are possible. The “recommendarlab” package is needed for this analysis and we will use the ratings of movies from grouplens.org for this post.

url http://grouplens.org/datasets/movielens/latest/

If you follow along you want to download the “small dataset” and use the “ratings.csv” and the “movies.csv”. We will then merge these two datasets based on the variable “movieId” the url is below is the initial code

library(recommenderlab) ratings <- read.csv("~/Downloads/ml-latest-small/ratings.csv")#load ratings data
movies <- read.csv("~/Downloads/ml-latest-small/movies.csv")#load movies data
movieRatings<-merge(ratings, movies, by='movieId')#merge movies and ratings data

We now need to convert are “movieRatings” data frame to a matrix that the “recommendarlab” can use. After doing this we need to indicate that we are doing a binary engine by setting the minimum rating to 2.5. What this means is that anything above 2.5 is in one category and anything below 2.5 is in a different category. We use the “binarize” function to do this. Below is the code

movieRatings<-as(movieRatings,"realRatingMatrix")
movie.bin<-binarize(movieRatings,minRating=2.5)

We need to use a subset of our data. We need each row to have a certain minimum number of ratings. For this analysis, we need at least ten ratings per row. Below is the code for this.

movie.bin<-movie.bin[rowCounts(movie.bin)>10]
movie.bin
## 1817 x 671 rating matrix of class 'binaryRatingMatrix' with 68643 ratings.

Next, we need to setup the evaluation scheme. We use the function and plug in the data, method of evaluation, number of folds, and the given number of ratings. The code is as follows.

set.seed(456)
e.bin<-evaluationScheme(movie.bin,method='cross-validation',k=5,given=10)

We now make a list that holds all the models we want to run. We will run four models “popular”, “random”, “ubcf”, and “ibcf”. We will then use the “evaluate” function to see how accurate are models are for 5,10,15, and 20 items.

algorithms.bin<-list(POPULAR=list(name="POPULAR",param=NULL),
                     RAND=list(name="RANDOM"),UBCF=list(name="UBCF"),IBCF=list(name="IBCF")) 
results.bin<-evaluate(e.bin,algorithms.bin,n=c(5,10,15,20))

The “avg” function will help us to see how are models did. Below are the results

avg(results.bin)
## $POPULAR
##          TP        FP       FN       TN precision     recall        TPR
## 5  1.518356  3.481644 26.16877 629.8312 0.3036712 0.09293487 0.09293487
## 10 2.792329  7.207671 24.89479 626.1052 0.2792329 0.15074799 0.15074799
## 15 3.916164 11.083836 23.77096 622.2290 0.2610776 0.20512093 0.20512093
## 20 4.861370 15.138630 22.82575 618.1742 0.2430685 0.24831787 0.24831787
##            FPR
## 5  0.005426716
## 10 0.011221837
## 15 0.017266489
## 20 0.023608749
## 
## $RAND
##           TP        FP       FN       TN  precision      recall
## 5  0.2120548  4.787945 27.47507 628.5249 0.04241096 0.007530989
## 10 0.4104110  9.589589 27.27671 623.7233 0.04104110 0.015611349
## 15 0.6241096 14.375890 27.06301 618.9370 0.04160731 0.023631305
## 20 0.8460274 19.153973 26.84110 614.1589 0.04230137 0.033130430
##            TPR         FPR
## 5  0.007530989 0.007559594
## 10 0.015611349 0.015146399
## 15 0.023631305 0.022702057
## 20 0.033130430 0.030246522
## 
## $UBCF
##          TP        FP       FN       TN precision    recall       TPR
## 5  2.175890  2.824110 25.51123 630.4888 0.4351781 0.1582319 0.1582319
## 10 3.740274  6.259726 23.94685 627.0532 0.3740274 0.2504990 0.2504990
## 15 5.054795  9.945205 22.63233 623.3677 0.3369863 0.3182356 0.3182356
## 20 6.172603 13.827397 21.51452 619.4855 0.3086301 0.3748969 0.3748969
##            FPR
## 5  0.004387006
## 10 0.009740306
## 15 0.015492088
## 20 0.021557381
## 
## $IBCF
##          TP        FP       FN       TN precision     recall        TPR
## 5  1.330411  3.669589 26.35671 629.6433 0.2660822 0.08190126 0.08190126
## 10 2.442192  7.557808 25.24493 625.7551 0.2442192 0.13786523 0.13786523
## 15 3.532603 11.467397 24.15452 621.8455 0.2355068 0.19010813 0.19010813
## 20 4.546301 15.453699 23.14082 617.8592 0.2273151 0.23494969 0.23494969
##            FPR
## 5  0.005727386
## 10 0.011801682
## 15 0.017900255
## 20 0.024124329

The results are pretty bad for all models. The TPR (true positive rate) is always below .4. We can make a visual of the results by creating a ROC using the TPR/FPR as well as precision/recall.

plot(results.bin,legend="topleft",annotate=T)

1.png

plot(results.bin,"prec",legend="topleft",annotate=T)

1.png

The visual makes it clear that the UBCF model is the best.

Conclusion

This post provided an example of the development of an algorithm for binary recommendations.

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