predict.FRM.Rd
Make Predictions from an "FRM" Object
# S3 method for FRM
predict(
model,
data,
key = NULL,
user.info = NULL,
item.info = NULL,
thread.ratio = NULL
)
S3
methods
R6Class object
A "FRM" object for prediction.
DataFrame
DataFrame containting data of user-item interaction and global
side features, structured as follows:
Data ID column(optional).
USER ID column.
ITEM ID column.
Side feature columns.
character, optional
Name of the ID column.
If not provided, the data is assumed to have no ID column.
No default value.
DataFrame
DataFrame containting information of side features about user,
structured as follows:
USER ID column.
Side feature columns.
DataFrame
DataFrame containing information of side features about item,
structured as follows:
ITEM ID column.
Side feature columns.
double, optional
Controls the proportion of available threads that can be used by this
function.
The value range is from 0 to 1, where 0 indicates a single thread,
and 1 indicates all available threads.
Values between 0 and 1 will use up to
that percentage of available threads.Values outside this
range are ignored.
Defaults to 0.
DataFrame
Prediction made by the model, structured as follows:
ID column
USER ID column
ITEM ID column
Predicted rating value.
Input DataFrames:
> predict.data$Collect()
ID USER MOVIE TIMESTAMP
1 1 A Movie3 NA
2 2 A Movie7 4
3 3 B Movie1 2
4 4 B Movie6 3
5 5 C Movie3 2
6 6 D Movie2 1
7 7 D Movie5 3
8 8 E Bad_Movie 2
> user.info$Collect()
USER USER_SIDE_FEATURE
1 NA NA
> item.info$Head(5)$Collect()
MOVIE GENRES
1 Movie1 Sci-Fi
2 Movie2 Drama,Romance
3 Movie3 Drama,Sci-Fi
4 Movie4 Crime,Drama
5 Movie5 Crime,Drama
Call the function with a "FRM" object fm:
result <- predict(model = fm,
predict.data = predict.data,
user.info = user.info,
item.info = item.info)
Output:
> result$Collect()
ID USER ITEM PREDICTION
1 1 A Movie3 3.705492
2 2 A Movie7 3.401231
3 3 B Movie1 5.457519
4 4 B Movie6 5.815021
5 5 C Movie3 3.421293
6 6 D Movie2 4.441541
7 7 D Movie5 4.314721
8 8 E Bad_Movie 2.585036