Score_replace: Replacing of Neighbor Users' Ratings on Non-Rated Items By...

In CFF: Simple Similarity for User-Based Collaborative Filtering Systems

Description

The ratings of each user that has more similar to the active user are directly replaced in his unseen items.

Usage

Score_replace(ratings, sim_index, ac)

Arguments

ratings	A rating matrix whose rows are items and columns are users.
sim_index	Descending sorted indexes based on similarity to the active user who is a vector of integers.
ac	The id of an active user as an integer (1≤ ac ≤ length of users).

Details

The unseen items of the active user are filled by the ratings of the similar users, respectively. Each element remains unchanged after one placement.

Value

ratings2

A matrix the size of the original user-item matrix in which the active user's empty elements are filled.

Author(s)

Farimah Houshmand Nanehkaran

Maintainer: Farimah Houshmand Nanehkaran <[e-mail]>

References

Gadekula, S. K., Rao, U. P., Vyas, R. K., Dontula, A. L., & Gaikwad, S. V. (2019). Improved Pearson Similarity for Collaborative Filtering Recommendation System. In 2019 6th International Conference on Computing for Sustainable Global Development (INDIACom), pp. 1047-1054, 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)

sim <- simple_similarity(ratings, max_score=5, min_score=1, ac=1)

ratings2 <- Score_replace(ratings, sim_index= sim$sim_index, ac=1)

CFF documentation built on March 13, 2020, 2:09 a.m.