GACFF (version 1.0)

Prediction: prediction function

Description

Obtaining the ratings of items that not seen by the active user.

Usage

Prediction (ratings, active_user, near_user, sim_x, 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).

near_user

Neighbor users.

sim_x

Similarity of neighbor users obtained from Similarity function.

KNN

The number of neighbor users that obtained for the active user from function or manually.

Value

pre_y

A set of predicted ratings for all items of the active user.

Details

The prediction formula is: $$(p_x)^i=\bar r_x + ((\sum_(n\in near users)([sim(u_x, u_n).((r_n)^i - (\bar r)_n)]))/(\sum_(n\in near users)(|sim(u_x, u_n)|)))$$

where \((P_x)^i\) is the prediction of the user x to an item i. \((\bar r)_x\) is the average ratings of the user x and \(\bar r_n\) is the average ratings of neighbors.

References

Moses, J.S. and Babu, L.D. (2018). Evaluating Prediction Accuracy, Developmental Challenges, and Issues of Recommender Systems. International Journal of Web Portals (IJWP), vol. 10, no. 2, pp. 61-79.

Singh, P., Ahuja, S. and Jain, S. (2019). Latest Trends in Recommender Systems 2017. In Advances in Data and Information Sciences, pp. 197-210. Springer, Singapore.

Examples

# NOT RUN {
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)

predict <- Prediction (ratings, active_user=6, 
                       near_user=Pearson.out$near_user_Pearson, 
                       sim_x=Pearson.out$sim_Pearson, 
                       KNN=length(Pearson.out$sim_Pearson))
# }