Before building and evaluating recommender systems using the two data sets we have loaded, it is a good idea to get a feel for the data. For one thing, we can make use of the getRatings() function to retrieve the ratings from a rating matrix. This is useful in order to construct a histogram of item ratings. Additionally, we can also normalize the ratings with respect to each user as we discussed earlier. The following code snippet shows how we can compute ratings and normalized ratings for the jester data. We can then do the same for the MovieLens data and produce histograms for the ratings:
> jester_ratings <- getRatings(jester_rrm)
> jester_normalized_ratings <- getRatings(normalize(jester_rrm,
method = "Z-score"))The following plot shows the different histograms:
In the jester data, we can see that ratings above zero are more prominent than ratings below zero, and the most common rating is 10, the maximum rating....
