August 2017
Intermediate to advanced
288 pages
8h 6m
English
Content preview from R Deep Learning Cookbook
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How to do it...
This recipe covers the steps for evaluating the output from RBM-based collaborative filtering:
- Select the ratings of a user:
inputUser = as.matrix(t(trX[75,]))names(inputUser) <- movies_df$id_order
- Remove the movies that were not rated by the user (assuming that they have yet to be seen):
inputUser <- inputUser[inputUser>0]
- Plot the top genres seen by the user:
top_rated_movies <- movies_df[as.numeric(names(inputUser)[order(inputUser,decreasing = TRUE)]),]$Titletop_rated_genres <- movies_df[as.numeric(names(inputUser)[order(inputUser,decreasing = TRUE)]),]$Genrestop_rated_genres <- as.data.frame(top_rated_genres,stringsAsFactors=F)top_rated_genres$count <- 1top_rated_genres <- aggregate(count~top_rated_genres,FUN=sum,data=top_rated_genres) ...