Results {GACFF}

Results of all active users.

Description

comparison of three methods (Genetic, NewKNN, Pearson) about MAE, elapsed time and predicted items.

Usage

Results(ratings, active_users, Threshold_KNN, max_scour, min_scour,
        PopSize=100, MaxIteration=50, CrossPercent=70, MutatPercent=20)

Arguments

ratings

A rating matrix whose rows are items and columns are users.

active_users

A vector of all active users id.

Threshold_KNN

Maximum number of neighbors.

max_scour

The maximum range of ratings.

min_scour

The minimum range of ratings.

PopSize

Population size (Number of chromosomes) in Genetic algorithm.

MaxIteration

Number of iterations in Genetic algorithm.

CrossPercent

Percentage of the Genetic algorithm population that participates in the Single-point crossover operator to generate new offspring.

MutatPercent

Percentage of the Genetic algorithm population that participates in the mutation.

Details

MAE is the average of the difference between real ratings of the active user and predicted ratings obtained from a method.

Value

An object of class "Results", a list with components:

call

The call used.

MAE_Pearson

MAE obtained from the "Pearson" method.

MAE_NewKNN

MAE obtained from the "NewKNN" method.

MAE_Genetic

MAE obtained from the "Genetic" method.

MAE_GA

A vector of MAE in every "Genetic" iteration.

time_Pearson

Elapsed time of the "Pearson" method.

time_NewKNN

Elapsed time of the "NewKNN" method.

time_Genetic

Elapsed time of the "Genetic" method.

Note

MAE is abbreviate of Mean Absolute Error.

References

Salehi, M. (2014). Latent feature based recommender system for learning materials using genetic algorithm. Information Systems & Telecommunication, vol. 137.

See Also

Genetic, NewKNN, Pearson.

Examples

ratings<-matrix(c(  2,    5,  NaN,  NaN,  NaN,    4,
                  NaN,  NaN,  NaN,    1,  NaN,    5,
                  NaN,    4,    5,  NaN,    4,  NaN,
                    4,  NaN,  NaN,    5,  NaN,  NaN,
                    5,  NaN,    2,  NaN,  NaN,  NaN,
                  NaN,    1,  NaN,    4,    2,  NaN),nrow=6,byrow=TRUE)

active_users <- c(1:dim(ratings)[2])

Results.out <- Results(ratings, active_users, Threshold_KNN=4, max_scour=5, 
                     min_scour=1, PopSize=100, MaxIteration=50, 
                     CrossPercent=70, MutatPercent=20)