Description Usage Arguments Details Value References Examples
Obtaining the ratings of items that not seen by the active user.
1 | Prediction (ratings, active_user, near_user, sim_x, KNN)
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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. |
The prediction formula is:
(p_x)^i=\bar r_x + ((∑_(n\in near users)([sim(u_x, u_n).((r_n)^i - (\bar r)_n)]))/(∑_(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.
pre_y |
A set of predicted ratings for all items of the active user. |
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.
1 2 3 4 5 6 7 8 9 10 11 12 13 | 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))
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