simple_predict {CFF}

Prediction Unseen Items For The Active User

Description

In the predicted items list, items with more scores replace in top of the list.

Usage

simple_predict(ratings, ratings2, ac)

Arguments

ratings

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

ratings2

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

ac

The id of an active user as an integer (1\le ac \le length of users).

Details

Collaborative filtering is a recommender system for predicting the missing ratings that an active user might have given to an item. These ratings have been calculated and accumulate in a vector by this function.

Value

predictedItems

A sorted vector of predicted items based on the scores.

Author(s)

Farimah Houshmand Nanehkaran

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

References

Song, B., Gao, Y., & Li, X. M. (2020, January). Research on Collaborative Filtering Recommendation Algorithm Based on Mahout and User Model. In Journal of Physics: Conference Series, Vol. 1437, no. 1, p. 012095, IOP Publishing.

Ramakrishnan, G., Saicharan, V., Chandrasekaran, K., Rathnamma, M. V., & Ramana, V. V. (2020). Collaborative Filtering for Book Recommendation System. In Soft Computing for Problem Solving, pp. 325-338, Springer, Singapore.

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)

predictedItems <- simple_predict(ratings, ratings2, ac=1)