Once a model has been trained rrecsys may be used to generate either recommendation or predictions.
The prediction method will generate a new rating matrix with estimations on the missing ratings. Let’s predict using two models trained in the previous vignettes:
pSVD <- predict(svd)
pIB <- predict(ibknn)
# Lets compare predicted results for user 1, 4 and 7 on two movies:
pSVD[c(1,4,6), c("Jumanji (1995)", "GoldenEye (1995)")]
## Jumanji (1995) GoldenEye (1995)
## [1,] 2.6229342 2.9577780
## [2,] 1.2022907 1.3334926
## [3,] 0.7943465 0.8768874
pIB[c(1,4,6), c("Jumanji (1995)", "GoldenEye (1995)")]
## Jumanji (1995) GoldenEye (1995)
## [1,] 3.013517 3.685114
## [2,] 3.000000 3.289993
## [3,] 3.830333 3.830333
The predict method has a second argument, Round that rounds predicted values to the scale and binds them to the domain of the data set.
The recommend method generates a top-N list for each user:
rSVD <- recommend(svd, topN = 3)
rIB <- recommend(ibknn, topN = 3)
# Let’s compare results on user 3:
rSVD[4]
## [[1]]
## [1] "Pulp Fiction (1994)"
## [2] "Star Wars: Episode IV - A New Hope (1977)"
## [3] "Forrest Gump (1994)"
rIB[4]
## [[1]]
## [1] "Star Wars: Episode IV - A New Hope (1977)"
## [2] "Forrest Gump (1994)"
## [3] "Star Wars: Episode V - The Empire Strikes Back (1980)"
The topN argument specifies the length of the recommended list for each user.