## Because You Are Interested In Data Science, You Are Interested In This Blog Post

If you love streaming movies and tv series online as much as we do here at STATWORX, you’ve probably stumbled upon recommendations like “Customers who viewed this item also viewed…” or “Because you have seen …, you like …”. Amazon, Netflix, HBO, Disney+, etc. all recommend their products and movies based on your previous user behavior – But how do these companies know what their customers like? The answer is collaborative filtering.

In this blog post, I will first explain how collaborative filtering works. Secondly, I’m going to show you how to develop your own small movie recommender with the R package `recommenderlab`

and provide it in a shiny application.

## Different Approaches

There are several approaches to give a recommendation. In the **user-based collaborative filtering** (UBCF), the users are in the focus of the recommendation system. For a new proposal, the similarities between new and existing users are first calculated. Afterward, either the n most similar users or all users with a similarity above a specified threshold are consulted. The average ratings of the products are formed via these users and, if necessary, weighed according to their similarity. Then, the x highest rated products are displayed to the new user as a suggestion.

For the **item-based collaborative filtering** IBCF, however, the focus is on the products. For every two products, the similarity between them is calculated in terms of their ratings. For each product, the k most similar products are identified, and for each user, the products that best match their previous purchases are suggested.

Those and other collaborative filtering methods are implemented in the `recommenderlab`

package:

**ALS_realRatingMatrix**: Recommender for explicit ratings based on latent factors, calculated by alternating least squares algorithm.**ALS_implicit_realRatingMatrix**: Recommender for implicit data based on latent factors, calculated by alternating least squares algorithm.**IBCF_realRatingMatrix**: Recommender based on item-based collaborative filtering.**LIBMF_realRatingMatrix**: Matrix factorization with LIBMF via package recosystem.**POPULAR_realRatingMatrix**: Recommender based on item popularity.**RANDOM_realRatingMatrix**: Produce random recommendations (real ratings).**RERECOMMEND_realRatingMatrix**: Re-recommends highly-rated items (real ratings).**SVD_realRatingMatrix**: Recommender based on SVD approximation with column-mean imputation.**SVDF_realRatingMatrix**: Recommender based on Funk SVD with gradient descend.**UBCF_realRatingMatrix**: Recommender based on user-based collaborative filtering.

## Developing your own Movie Recommender

### Dataset

To create our recommender, we use the data from movielens. These are film ratings from 0.5 (= bad) to 5 (= good) for over 9000 films from more than 600 users. The movieId is a unique mapping variable to merge the different datasets.

`head(movie_data)`

```
movieId title genres
1 1 Toy Story (1995) Adventure|Animation|Children|Comedy|Fantasy
2 2 Jumanji (1995) Adventure|Children|Fantasy
3 3 Grumpier Old Men (1995) Comedy|Romance
4 4 Waiting to Exhale (1995) Comedy|Drama|Romance
5 5 Father of the Bride Part II (1995) Comedy
6 6 Heat (1995) Action|Crime|Thriller
```

`head(ratings_data)`

```
userId movieId rating timestamp
1 1 1 4 964982703
2 1 3 4 964981247
3 1 6 4 964982224
4 1 47 5 964983815
5 1 50 5 964982931
6 1 70 3 964982400
```

To better understand the film ratings better, we display the number of different ranks and the average rating per film. We see that in most cases, there is no evaluation by a user. Furthermore, the average ratings contain a lot of “smooth” ranks. These are movies that only have individual ratings, and therefore, the average score is determined by individual users.

```
# ranting_vector
0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5
5830804 1370 2811 1791 7551 5550 20047 13136 26818 8551 13211
```

In order not to let individual users influence the movie ratings too much, the movies are reduced to those that have at least 50 ratings.

```
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 2.208 3.444 3.748 3.665 3.944 4.429
```

Under the assumption that the ratings of users who regularly give their opinion are more precise, we also only consider users who have given at least 50 ratings. For the films filtered above, we receive the following average ratings per user:

You can see that the distribution of the average ratings is left-skewed, which means that many users tend to give rather good ratings. To compensate for this skewness, we normalize the data.

`ratings_movies_norm <- normalize(ratings_movies)`

## Model Training and Evaluation

To train our recommender and subsequently evaluate it, we carry out a 10-fold cross-validation. Also, we train both an IBCF and a UBCF recommender, which in turn calculate the similarity measure via cosine similarity and Pearson correlation. A random recommendation is used as a benchmark. To evaluate how many recommendations can be given, different numbers are tested via the vector `n_recommendations`

.

```
eval_sets <- evaluationScheme(data = ratings_movies_norm,
method = "cross-validation",
k = 10,
given = 5,
goodRating = 0)
models_to_evaluate <- list(
`IBCF Cosinus` = list(name = "IBCF",
param = list(method = "cosine")),
`IBCF Pearson` = list(name = "IBCF",
param = list(method = "pearson")),
`UBCF Cosinus` = list(name = "UBCF",
param = list(method = "cosine")),
`UBCF Pearson` = list(name = "UBCF",
param = list(method = "pearson")),
`Zufälliger Vorschlag` = list(name = "RANDOM", param=NULL)
)
n_recommendations <- c(1, 5, seq(10, 100, 10))
list_results <- evaluate(x = eval_sets,
method = models_to_evaluate,
n = n_recommendations)
```

We then have the results displayed graphically for analysis.

We see that the best performing model is built by using UBCF and the Pearson correlation as a similarity measure. The model consistently achieves the highest true positive rate for the various false-positive rates and thus delivers the most relevant recommendations. Furthermore, we want to maximize the recall, which is also guaranteed at every level by the **UBCF Pearson** model. Since the n most similar users (parameter `nn`

) are used to calculate the recommendations, we will examine the results of the model for different numbers of users.

```
vector_nn <- c(5, 10, 20, 30, 40)
models_to_evaluate <- lapply(vector_nn, function(nn){
list(name = "UBCF",
param = list(method = "pearson", nn = vector_nn))
})
names(models_to_evaluate) <- paste0("UBCF mit ", vector_nn, "Nutzern")
list_results <- evaluate(x = eval_sets,
method = models_to_evaluate,
n = n_recommendations)
```

## Conclusion

Our user based collaborative filtering model with the Pearson correlation as a similarity measure and 40 users as a recommendation delivers the best results. To test the model by yourself and get movie suggestions for your own flavor, I created a small Shiny App.

However, there is no guarantee that the suggested movies really meet the individual taste. Not only is the underlying data set relatively small and can still be distorted by user ratings, but the tech giants also use other data such as age, gender, user behavior, etc. for their models.

But what I can say is: Data Scientists who read this blog post also read the other blog posts by STATWORX.

## Shiny-App

Here you can find the Shiny App. To get your own movie recommendation, select up to 10 movies from the dropdown list, rate them on a scale from 0 (= bad) to 5 (= good) and press the run button. Please note that the app is located on a free account of shinyapps.io. This makes it available for 25 hours per month. If the 25 hours are used and therefore the app is this month no longer available, you will find the code here to run it on your local RStudio.

# ABOUT US

STATWORX

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