Before building and evaluating recommender systems using the two datasets 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 ...