Pearson {GACFF}

Pearson method

Description

The Pearson method is the most well-known method for finding users' similarity, so to compare the genetic-based method, the Pearson method has been implemented in this package.

Usage

Pearson(ratings, active_user, Threshold_KNN)

Arguments

ratings

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

active_user

The id of an active user as an integer greater than zero (for example active_user<-6).

Threshold_KNN

Maximum number of neighbor users.

Details

Pearson Correlation Coefficient (PCC) is the similarity measure for Collaborative filtering recommender system, to evaluate how much two users are correlated [3].

Value

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

call

The call used.

sim_Pearson

The similarity of the Pearson method.

pre_Pearson

The prediction of the Pearson method.

item_Pearson

A list of recommended items by the Pearson method.

near_user_Pearson

Neighbors of active user in the Pearson method orderly.

time_Pearson

The elapsed time of the Pearson method.

References

[1] Bobadilla, J., Ortega, F., Hernando, A. and Alcala, J. (2011). Improving collaborative filtering recommender system results and performance using genetic algorithms. Knowledge-based systems, vol. 24, no. 8, pp. 1310-1316.

[2] Lu, J., Wu, D., Mao, M., Wang W. and Zhang, G. (2015). Recommender system application developments: a survey. Decision Support Systems, vol. 74, pp. 12-32.

[3] Sheugh, L. and Alizadeh, S.H. (2015). A note on pearson correlation coefficient as a metric of similarity in recommender system. In 2015 AI & Robotics (IRANOPEN) (pp. 1-6). IEEE.

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)

Pearson.out  <- Pearson (ratings, active_user=6, Threshold_KNN=4)