Results: Results of all active users.

In GACFF: Genetic Similarity in User-Based Collaborative Filtering

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)

GACFF documentation built on Dec. 20, 2019, 5:07 p.m.